Podcasts about cetp

In this podcast, expert clinicians will discuss the latest evidence regarding the role of cholesteryl ester transfer protein (CETP) inhibitors in the management of patients with dyslipidemia. https://healio.com/cme/mededtalks/cardiology/20241104/2-lipid-legends-with-your-host-dr-ronald-codario

Audio roundup of selected biopharma industry content from Scrip over the business week ended 2 August 2024. In this episode: Pfizer bullish on its oral GLP-1; BMS's rising confidence about Medicare pricing of Eliquis; more long term data for Leqembi; NewAmsterdam's CETP inhibitor shows promise; and a look at Mankind's Bharat Serums buy. https://scrip.citeline.com/SC150698/Quick-Listen-Scrips-Five-MustKnow-Things Playlist: soundcloud.com/citelinesounds/sets/scrips-five-must-know-things

View the Show Notes Page for This Episode Become a Member to Receive Exclusive Content Sign Up to Receive Peter's Weekly Newsletter John Kastelein is a renowned expert in lipoprotein metabolism and atherosclerotic cardiovascular disease (ASCVD) research. In this discussion, John delves deep into familial hypercholesterolemia (FH), a genetic disorder characterized by high levels of LDL cholesterol in the blood that increases the risk of developing heart disease. He covers its definition, genetic underpinnings, and clinical identification. He then explores the therapeutic options available for the prevention and treatment of cardiovascular disease, including the captivating history of CETP inhibitors. He explains the past shortcomings of previous CETP inhibitors before underscoring the compelling potential of the latest iterations, not only for cardiovascular disease but also for conditions like Alzheimer's disease and type 2 diabetes. Moreover, he unveils the intricate role of APOE, shedding light on why the APOE4 isoform codes for a protein that significantly increases the risk of Alzheimer's disease and cardiovascular disease. Concluding the discussion, John shares a profound sense of optimism, envisioning the possibility of targeted therapeutic interventions for high-risk patients in the near future. We discuss: Familial hypercholesterolemia (FH): a genetic condition [4:30]; Differentiating between phenotype and genotype when it comes to FH [9:45]; The pathophysiology related to mutations of FH [15:30]; Clinical presentations, physical manifestations, and diagnosis of FH [22:00]; Why a small fraction of people with FH do not develop premature ASCVD [34:15]; Treatment and prevention for those with FH [39:45]; Addressing the assertion by some that elevated LDL is not casual in cardiovascular disease [52:45]; The history of CETP inhibitors, and the role of the CETP protein [55:45]; The thrifty gene hypothesis and why genes underlying FH may have been preserved [1:09:00]; The compelling potential of the latest CETP inhibitor (obicetrapib) [1:13:00]; Promising results from phase 3 trials exploring obicetrapib [1:27:45]; Why the APOE4 allele increases the risk of Alzheimer's disease, and the connection to blood lipids [1:41:30]; The role of APOE in cardiovascular disease [1:51:45]; Takeaways and looking ahead [1:57:00]; and More. Connect With Peter on Twitter, Instagram, Facebook and YouTube

View the Show Notes Page for This Episode Become a Member to Receive Exclusive Content Sign Up to Receive Peter's Weekly Newsletter Dan Rader is a Professor at the Perelman School of Medicine at the University of Pennsylvania, where he conducts translational research on lipoprotein metabolism and atherosclerosis with a particular focus on the function of high-density lipoproteins (HDLs). In this episode, Dan goes in-depth on HDL biology, including the genesis of HDL, its metabolism, function, and how this relates to atherosclerotic cardiovascular disease (ASCVD). He explains why having high HDL-C levels does not directly translate to a low risk of cardiovascular disease and reveals research pointing to a better way to measure the functionality of HDL and predict disease risk. He also goes into detail on the role of HDL in reverse cholesterol transport and the benefits this has for reducing ASCVD. Additionally, Dan discusses the latest thinking around the association between HDL cholesterol and neurodegenerative diseases and ends the conversation with a discussion of how the latest research on HDL provides a promising outlook for ongoing trials and future therapeutic interventions. We discuss: The lipidology of apoB and apoA [4:00]; A primer on the high-density lipoprotein (HDL): genesis, structure, and more [9:30]; How the lipoprotein system differs in humans compared to other mammals [20:00]; Clarifying the terminology around HDL and apoA [25:30]; HDL metabolism [31:45]; CETP inhibitors for raising HDL-C: does it reduce CVD risk? [34:45]; Why it's so important to have hard outcome trials in the field of cardiovascular medicine [42:30]; SR-B1: an HDL receptor important for cholesterol efflux [48:00]; The association between HDL levels and atherosclerosis: are they causally linked? [53:15]; How insulin resistance is impacting HDL, and how HDL-C provides insights into triglyceride metabolism [58:00]; Disappointing results from the studies of niacin—a drug that raises HDL-C and lowers apoB [1:08:15]; HDL lipidation, dilapidation, and reverse cholesterol transport [1:12:00]; Measuring the cholesterol efflux capacity of HDL: a better predictor of ASCVD risk than HDL-C? [1:22:00]; A promising new intervention that may promote cholesterol efflux and reverse cholesterol transport [1:32:45]; The association between HDL cholesterol and neurodegenerative diseases [1:34:00]; Challenges ahead, a promising outlook, and the next frontier in lipidology [1:44:45]; and More. Connect With Peter on Twitter, Instagram, Facebook and YouTube

What does the body of evidence say on the topic of cholesterol? Plus: we look at the claim that a multivitamin can preserve your cognitive functions as you age, and we cover that study that allegedly shows aluminum in vaccines causes asthma!   Block 1: (3:04) Cholesterol: what cholesterol is, total cholesterol, LDL cholesterol, how fat is digested, HDL cholesterol   Block 2: (10:44) Cholesterol: triglycerides, how LDL is calculated, do you need to fast before a blood test, attempts at increasing HDL, how to lower LDL   Block 3: (22:38) Cocoa extract or multivitamin for preserving cognition (and a digression on cocoa vs cacao)   Block 4: (39:13) Aluminum in vaccines and a reported link to asthma     * Theme music: “Fall of the Ocean Queen“ by Joseph Hackl. * Assistant researcher: Nicholas Koziris   To contribute to The Body of Evidence, go to our Patreon page at: http://www.patreon.com/thebodyofevidence/.   To make a one-time donation to our show, you can now use PayPal! https://www.paypal.com/donate?hosted_button_id=9QZET78JZWCZE   Patrons get a bonus show on Patreon called “Digressions”! Check it out!     References:   1) Systematic review and meta-analysis on reducing LDL cholesterol: https://jamanetwork.com/journals/jama/fullarticle/2556125   2) Safety of lowering LDL cholesterol in patients starting with very low levels: https://jamanetwork.com/journals/jamacardiology/fullarticle/2695047   3) HPS2-THRIVE trial on niacin + laropiprant: https://doi.org/10.1056/nejmoa1300955   4) AIM-HIGH trial on niacin: https://doi.org/10.1056/nejmoa1107579   5) CETP inhibitor torcetrapib increases mortality: https://doi.org/10.1056/nejmoa0706628   6) Meta-analysis of the data on boosting HDL levels: https://doi.org/10.1136/bmj.g4379   7) The COSMOS-Mind paper: https://doi.org/10.1002/alz.12767   8) Our episode on chocolate: https://bodyofevidence.ca/013-chocolate-and-influenza   9) The original COSMOS papers: https://doi.org/10.1093/ajcn/nqac055 & https://doi.org/10.1093/ajcn/nqac056   10) The USPS statement on vitamin supplements: https://www.uspreventiveservicestaskforce.org/uspstf/recommendation/vitamin-supplementation-to-prevent-cvd-and-cancer-preventive-medication   11) Cochrane reviews on vitamins and cognition: https://www.cochranelibrary.com/cdsr/doi/10.1002/14651858.CD011906.pub2/full & https://www.cochranelibrary.com/cdsr/doi/10.1002/14651858.CD011905.pub2/full   12) Helen Branswell's STAT News coverage of the aluminum-asthma paper: https://www.statnews.com/2022/09/27/possible-link-aluminum-childhood-vaccines-asthma-risk-caveats/   13) The aluminum-asthma paper: https://els-jbs-prod-cdn.jbs.elsevierhealth.com/pb/assets/raw/Health%20Advance/journals/acap/Aluminium_Exposure_Article-1664288052690.pdf   14) Our episode on asthma: https://bodyofevidence.ca/080-asthma-screen-time-exercise-motivation     Time machine:   1) Our episode on fat: https://bodyofevidence.ca/015-fat-and-denialism   2) Our episode on dementia: https://bodyofevidence.ca/079-dementia-long-covid-no-vax-tax  

Vitamin K, specifically vitamin K2, has been almost universally hyped by social media's health influencers for the past 10 years as a cheap, risk-free, alternative preventive therapy for cardiovascular disease. But what evidence is there that it actually works?Many highly plausible preventive therapies for cardiovascular disease have been shown not to work over the past 50 years. In many cases they have been shown to be harmful. Starting with anti-arrythmics, which were shown in the CAST trial to dramatically increase mortality in heart attack survivors despite having been used for over a decade prior (likely the cause of tens of thousands of deaths), the history of plausible mechanisms for cardiovascular disease prevention is grim indeed. Among these can be included homocysteine-lowering therapies (which may increase rates of some cardiovascular events), HDL-lowering therapies like CETP inhibitors (which in some trials also increased cardiovascular events), and antioxidant therapies (which in some cases, such as with beta-carotene, increased rates of cancer). One thing can be said for certain: what seemed promising according to observational and mechanistic research has often proved deadly when tested in actual clinical trials.Could this also be true for vitamin K2 supplements?According to some of the prevailing theories of cardiovascular disease among researchers, the mechanism of action of vitamin K2 for preventing cardiovascular disease is nonsensical. After all, simply reducing arterial calcification does not address the primary cause of calcification: the existence--and healing--of atherosclerotic plaques. Indeed, formation of non-calcified plaques occurs prior to the presence of calcifications. Calcifications are part of the healing process--indeed, part of a healing process that is universal across many different types of injuries in mammalian biology. Calcifications may in fact stabilize atherosclerotic plaques. Correspondingly, statins are thought to act in part by acceleration the formation of calcified plaques, thereby protecting atherosclerotic lesions from destabilization and breaking off to form thromboembolism, a blood clot which blocks the artery--the cause of most cardiovascular events, including heart attacks. People who exercise also have a dose-response increase in calcified plaques--despite having lower rates of heart attacks.Vitamin K2 may reverse the formation of calcifications that are protecting the arterial vessels from the formation of thrombi due to atherosclerotic plaques. Vitamin K2 may convert dense calcifications--good--into spiculated, patchy calcifications--bad. In other words, vitamin K2 may increase the risk of cardiovascular events--and death.Or it may not. Without the actual hard outcomes evidence of randomized controlled trials, we simply do not know whether vitamin K2 is helpful or harmful. One thing is for sure: on the basis of a long history of failed treatments, unproven mechanistic speculations should never be the basis for the prevention of cardiovascular disease. Even more dangerous is in the more extreme parts of the alternative health industry, where vitamin K2 supplements are often promoted as alternatives to conventional treatment like statins, such as by influencer Ivor Cummins.This practice among health influencers must stop, and we must be more circumspect about the kind of evidence that we deem adequate to overhype a recommendation. The lives of actual human beings depend on this.(I will add references when I get the chance--needed to get this out. If you would like to find them before I have that opportunity, please watch the video on Youtube. Thank you for your patience.)===Like, comment, subscribe.For more, find me at:PODCAST The Kevin Bass ShowYOUTUBE https://www.youtube.com/user/kbassphiladelphiaWEBSITE http://thedietwars.comTWITTER https://twitter.com/kevinnbass/https://twitter.com/healthmisinfo/INSTAGRAM https://instagram.com/kevinnbass/TIKTOK https://tiktok.com/@kevinnbassAnd above all, please donate to support what I do:PATREON https://patreon.com/kevinnbass/DONATE https://thedietwars.com/support-me/

View the Show Notes Page for This Episode Become a Member to Receive Exclusive Content Sign Up to Receive Peter's Weekly Newsletter Nir Barzilai, Director of the Institute for Aging Research at the Albert Einstein College of Medicine, is back for his third appearance on The Drive. In this episode, Nir divulges insights into lifespan and healthspan through the lens of his extensive research on centenarians as well as the latest from the TAME trial (Targeting/Taming Aging with Metformin), a multi-center study investigating the concept that the multi-morbidities of aging can be delayed in humans. He discusses common gene variants found in centenarians, important pathways for longevity, and ultimately what we can learn from centenarians about extending lifespan while also trying to improve healthspan. Additionally, Nir goes into depth on metformin as a longevity tool for humans, including studies with positive and negative results. He discusses the impact metformin can have on exercise for both strength training and cardiovascular training, as well as future research facilitated by data from the TAME Trial. He also touches on epigenetic clocks and concludes with his take on the usefulness of NAD precursors as a potential gero-protective agent. We discuss: Insights from genetic studies of centenarians and twins [3:00]; Genes with protective variants that aid longevity [13:00]; The relationship between growth hormone and IGF-1 [22:45]; Use of growth hormone as a longevity tool [34:00]; Longevity genotypes: the role of APOE e2, Lp(a), Klotho, and CETP [41:45]; The correlation between high TSH and longevity [46:30]; Important pathways for longevity [52:00]; Insights from centenarian studies, nature vs. nurture, and more [59:00]; The contraction of morbidity that comes with improved healthspan [1:08:00]; Defining healthspan [1:13:13]; Unique perspectives and positive attitudes of centenarians [1:17:30]; Lessons to take away from centenarians [1:24:00]; Metformin overview: history, studies, and potential for gero-protection [1:28:45]; The TAME trial (Targeting Aging with Metformin) [1:39:00]; The challenge of studying metformin in animals models [1:46:45]; How data from the TAME trial could provide insights into biomarkers of aging and facilitate a future study on proteomics [1:53:30]; The search for biomarkers to identify who can benefit from treatment [2:00:30]; The impact of metformin on exercise, and finding the right indication for the use of metformin [2:10:30]; Are NAD precursors geroprotective? [2:21:30]; and More. Connect With Peter on Twitter, Instagram, Facebook and YouTube  

El tejido adiposo es un órgano endocrino que regula múltiples funciones en nuestro organismo. Diferenciamos dos tipos, el tejido adiposo blanco (TAB) y el tejido adiposo marrón (TAM). Este último tiene una función de termorregulación y va disminuyendo con la edad, siendo los recién nacidos quienes lo tienen en mayor proporción. El TAB se distribuye por todo el cuerpo, a nivel intraabdominal encontramos los mayores depósitos alrededor del omento (omental), del intestino (mesentérico) y de las áreas perirrenales (retroperitoneal), y a nivel subcutáneo la grasa se localiza sobre todo a nivel de las nalgas, los muslos y el abdomen. Pero además de estos depósitos mayoritarios existen otras áreas en el organismo donde encontra- mos TAB, distinguiendo depósitos a nivel pericardial, perivascular o periarterial, periarticular, retro-orbital, intramuscular, médula ósea y cara.Existe, además, un dimorfismo sexual en cuanto a la distribución de la grasa corporal. Así, en el sexo masculino hay una mayor acumulación de grasa en la parte superior del cuerpo, lo que se conoce como distri- bución androide o de tipo manzana, mientras que en el sexo femenino la grasa predomina en la parte inferior del cuerpo, refiriéndose como distribución ginoide o de tipo pera.Cuando hay un exceso de grasa corporal aumenta el riesgo de padecer muchas enfermedades, debido a que en este hay unas sustancias llamadas adipocinas que regulan muchísimos procesos del organismo: intervienen en la regulación de la ingesta y del balance energético (leptina), en la regulación de la presión sanguínea (angiotensinógeno), en la hemostasia vascular (PAI-1), en el metabolismo lipídico (RBP-4, CETP), en la homeostasis glucídica (adiponectina, resistina, visfatina), en la angiogénesis (VEGF), así como factores de crecimiento y proteínas de fase aguda y respuesta al estrés (hap- toglobulina, ̨1-acid glycoprotein) El número de adipocitos en el adulto se mantiene relativamente constante, fijándose su número durante la infancia y la adolescencia, por este motivo es tan importante la alimentación infantil y crear bueno hábitos desde pequeños. Fuente: Ràfols, M.E. Tejido adiposo: heterogeneidad celular y diversidad funcional. Endocrinología y Nutrición, February 2014; 61, 2:100-112

Leo's channel: https://www.youtube.com/c/LeoandLongevity Derek's channel: https://www.youtube.com/channel/UCoR7CHkMETs3ByOv74OAbFw Steve's channel: https://www.youtube.com/user/VigorousSteve TIMESTAMPS: 0:00 intro 0:29 Connor Murphy/ Ayahuasca and DMT 1:36 Kenny KO and Connor 2:10 Leo on psychedelics/ his friend 3:38 Steve on psychedelics 5:29 Marijuana and schizophrenia/ More on Connor 7:45 Man cutting his genitals off on drugs 9:30 Screen sharing on Zoom 9:58 Leo's manic Canadian friend 10:47 Derek will avoid psychedelics 12:13 Derek on being ambitious 13:29 Losing your ego 14:12 How Leo hurt his finger 16:42 Antoine's Vaillant bicep and Olympia placing 19:06 Derek's bodybuilding genetics 20:13 Why Derek stopped doing steroids/ Making money as a bodybuilder 23:16 GH15, Antoine, and Frank Mcgrath 24:44 Bodybuilding, dieting, and tren 26:25 Recovery from injury Beta-blockers either before or after a surgery Nebivolol, collagen and gelatin protein, Propranolol, 29:03 Angiogenesis BPC 157, TB500, erythropoietin 31:06 Growth factors and hair loss/ Icing and cooling injuries 32:06 MK677, ghrelin  and surgeries/ MK677, GH, and IGF1 34:05 MK677, Ghrelin, PTSD, and Insulin 39:22 Jujimufu and Greg Ducette/Canadian accents 43:34 People being hyper-critical of people in the fitness industry. 44:49 Jujimufu, arm wrestling, and stomach distension 47:53 Leo's GH experience 49:31 Jujimufu's genetics 51:06 Looking like you work out while wearing a shirt 52:25 Anabolic pathways 53:30 Dallas McCarver autopsy/ Anthony Roberts ban 58:20 Dallas McCarver organs 1:00:21 Leo's friend taking large sums of steroids/ Derek on the autopsy 1:07:00 Derek and Steve on blood and urine drug tests/ Tren cough 1:11:10 What steroids do to your heart Dislipidemia, HDL goes down, HDLC decreases by approx 50%, APO A1 decreases by 33-41%, increases LDLC by approx 36% Reduce lipoprotein [a] 1:12:41 Homocysteine blood tests/ Chris Masterjohn  Creatine, Choline, B vitamins 1:13:50 More on Lipoprotein [a] Niacin, Repatha, and steroids 1:15:01 Derek's client with strange test results 1:15:54 Hypercholesterolemia  Homozygous APOCIII, CETP, and APOE4 1:17:33 Steroids, left ventricular systolic function, left ventricular diastolic function, and heart hypertrophy. 1:19:15 Heart FMRI 1:20:28 Impaired tonic cardiac autonomic regulation, and Clenbuterol 1:22:32 Leo's list of tests and genetics 1:23:25 Statins, Ezetimibe, and cholesterol 1:25:00 Automated gene searches 1:26:09 Statins and natural status 1:27:24 Lowering LDL and extending life PCSK9 inhibitors, Bempedoic acid, Ezetimibe, and Statins 1:28:41 Steve on Ezetimibe 1:29:32 Leo on Statins (the good and the bad) Pitavastatin, Rosuvastatin crestor ,livalo, lipitor 1:33:56 Telmisartan, Valsartan, Azilsartan and Irbesartan 1:39:17 Diuretics, bloating, and Estrogen 1:41:28 Hyperkalemia, Potassium and drug interactions 1:43:20 Minoxidil as a potassium channel opener and microneedling 1:45:49 Steve doesn't like hair 1:47:40 Leo's hypothesis on hair loss/ Derek on hair loss 1:54:25 Topical dutasteride 1:55:35 70-year-old women and balding 1:56:45 Steve on being secure with hair loss 2:00:40 Men and size 2:02:04 Pre-workout androgens Anadrol, Dianabol, Superdrol and Anadrol 2:08:26 Taking short-acting compounds around your workout 2:10:00 How steroids cause liver cancer and why Anavar doesn't cause it 2:11:56 Dianabol back pumps 2:13:19 Egyptian bodybuilders 2:14:52 Steve's fasting protocols 2:18:15 Reasons to fast 2:20:44 Leo's reasons to fast/ Satchin Panda's book/Valter Longo's fasting-mimicking diet and Prolon 2:23:47 Proper fasts on PEDs Allopurinol 2:27:36 How Steve and Leo prepare salads 2:30:35 The discord group 2:34:19 Unhealthy relations to Youtubers 2:39:58 Epigenetics and children 2:43:14 IVF and metabolic profiles 2:45:35 Coming off of testosterone and getting back to baseline 2:46:37 Having kids at an older age (epigenetic damage over time to sperm) 2:49:42 Steve and Leo on TV 2:50:15 Past downloading services 2:54:06 Unusual pre workout supplements for more strength or a better pump 2:56:40 Why the hell are people taking Phenibut and Kratom pre workout 2:58:00 Low dose Naltrexone therapy 3:00:16 Getting over addiction 3:02:34 Gynecomastia 3:05:43 Removing your glands before you take steroids/ Problems with Nolvadex 3:08:02 Derek and Steve on their gyno experiences 3:10:45 How to deal with gyno if you don't want the surgery 3:11:56 Steve on growing your gyno, to get the surgery JOIN OUR COMMUNITY: Reddit ▶ https://www.reddit.com/r/TheLongLived/ FOR GENETIC ANALYSIS & COACHING: Website ▶ https://www.leoandlongevity.com TO READ MY ARTICLES: Blog ▶ https://www.leoandlongevity.com/blog TO FOLLOW ME ON SOCIAL MEDIA:  Instagram ▶ https://www.instagram.com/leoandlongevity Twitter ▶ https://www.twitter.com/leoandlongevity

This week, join author authors John J.V. McMurrary and Milton Packer, and Associate Editor as they discuss their Perspective article "How Should We Sequence the Treatments for Heart Failure and a Reduced Ejection Fraction? A Redefinition of Evidence-Based Medicine." TRANSCRIPT BELOW Dr. Carolyn Lam: Welcome to Circulation on the Run, your weekly podcast summary and backstage pass to the journal and its editors. We're your co-hosts. I'm Dr. Carolyn Lam, associate editor from the National Heart Center and Duke National University of Singapore. Dr. Greg Hundley: And I'm Dr. Greg Hundley, associate editor, director of the Pauley Heart Center at VCU Health in Richmond, Virginia. Dr. Carolyn Lam: Greg, this feature discussion is going to knock you off your seat, because it did me. It's about treatment sequencing in HFrEF and discussing it with some luminaries on the field, Dr. John McMurray and Dr. Milton Packer. You are going to love it. I loved it. But I'm going to make you wait. How about you grab some coffee and let's start with some of the other papers in today's issue first. Dr. Greg Hundley: All right, Carolyn. How about if I go first? I'm going to start with a paper from Dr. Liam Brunham from the University of British Columbia. Well, Carolyn, the high density lipoprotein or HDL hypothesis of atherosclerosis has been challenged by clinical trials of cholesterol ester transfer protein, or CETP inhibitors, which failed to show significant reductions in cardiovascular events. Plasma levels of HDL cholesterol, or HDL-C, declined drastically during sepsis. And this phenomenon is explained in part by the activity of CETP, a major determinant of plasma HDL-C levels. So Carolyn, these authors tested the hypothesis that genetic or pharmacologic inhibition of CETP would preserve HDL levels and decrease mortality in clinical cohorts in animal models of sepsis. Dr. Carolyn Lam: Huh. Interesting. And what did they find? Dr. Greg Hundley: Well, Carolyn, results from both the human UK Biobank and the mouse model experiments suggested that inhibiting CETP may preserve HDL levels and improve outcomes for individuals with sepsis. Dr. Carolyn Lam: Wow. So is this ready for clinical applications somehow? Dr. Greg Hundley: Well, Carolyn, two conclusions from this work. First, high density, lipoprotein cholesterol, a commonly used biomarker for cardiovascular risk assessment, may also predict risk of death from sepsis. And then second, cholesterol ester transfer protein inhibitors that have been tested in clinical trials of cardiovascular disease could be repurposed and studied in clinical trials of sepsis. Dr. Carolyn Lam: Ooh, exciting. Well, Greg, for my paper, I'm going to ask you a question. Have you ever thought about what the temporal changes in medical prevention and adverse outcomes are in patients with symptomatic peripheral artery disease after revascularization? Well, wait no longer. Our next paper addresses that. It's from Dr. Sogaard from Aalborg University Hospital in Denmark and colleagues who identified all patients with a first open surgical or endovascular revascularization procedure in the lower extremities or abdomen in Denmark from 2000 to 2016. And this is what they found. Dr. Carolyn Lam: First, the profile of patients with PAD who underwent lower extremity revascularization changed towards older age and a higher prevalence of comorbidity. Despite increases in age and co-morbidity, medical prevention of adverse events improved substantially over time, particularly in the first part of the study period and among patients who used medications chronically. Dr. Carolyn Lam: Now in contrast, initiating treatment after revascularization increased modestly among treatment-naive patients. Now concurrently, prognosis improved for almost all adverse outcomes in patients of both sexes, all age groups, and in all high-risk co-morbidities. In particular, the risks of myocardial infarction and cardiovascular death declined by more than 40%. Dr. Greg Hundley: Well, Carolyn, are there any other findings with clinical implications here? Dr. Carolyn Lam: Yes. So that was the good news earlier. But despite overall improvements, significant disparities remain. Less than 40% of treatment-naive patients initiated cardioprotective therapy after revascularization, underscoring the need for raising levels of awareness and education in the vascular community, general practitioners and patients of this. Major amputations also remained unchanged and thus more work is needed to understand relationships between the preventive measures, revascularization and amputation. Dr. Greg Hundley: Great summary, Carolyn. My next paper comes from Dr. Rachael Cordina from the Royal Prince Alfred Hospital, University of Sydney. Neurocognitive outcomes beyond childhood in people with a Fontan circulation are not well-defined. So the investigators here aim to study neurocognitive functioning in adolescents and adults with a Fontan circulation and associations with structural brain injury, brain volumetry and postnatal clinical factors. Dr. Carolyn Lam: Okay, you got our attention. What did they find, Greg? Dr. Greg Hundley: Thanks, Carolyn. So participants with a Fontan circulation, without a pre-existing major neurological disability, were prospectively recruited from the Australia and New Zealand Fontan registry. And the investigators found that neurocognitive impairment is common in adolescents and adults with a Fontan circulation and is associated with smaller grey and white matter brain volume. Understanding, therefore Carolyn, modifiable factors that contribute to brain injury to optimize neurocognitive function is paramount. Dr. Carolyn Lam: Indeed. Well, this next paper I want to talk about is the first detailed endothelial cell cysteine-S self-hydrome. Dr. Greg Hundley: Self? S self-hydrome? What is that, Carolyn? Dr. Carolyn Lam: Good. I needed to catch your attention. Let me tell you about it. So in vascular endothelial cells, cysteine metabolism by cystathionine gamma-lyase, or CSE, generates hydrogen sulfide- related sulfane sulfur compounds. And these exert their biological actions via cysteine-S self-hydration of target proteins. So the paper we're talking about today by Dr. Fleming from Goethe University in Germany and colleagues, they aimed to map the S self-hydrome, which is the spectrum of proteins targeted by this hydrogen sulfide-related sulfane sulfur compounds, or H2Sn, in human endothelial cells. And they did this using liquid chromatography and tandem mass spectrometry. Dr. Carolyn Lam: So here's what they found: vascular disease was associated with mark changes in the S self-hydration of endothelial cell proteins involved in mediating responses to flow. Integrins were most effected by S self-hydration and the modification of beta-3 integrin resulted in reshuffling of the intramolecular disulfite bonds to preserve its extended and open confirmation. Loss of beta-3 integrin self-hydration, on the other hand, inhibited endothelial cell adhesion, impaired mechanosensing and attenuated flow induced phase with dilation. Thus, short term H2Sn supplementation could improve vascular reactivity in humans, highlighting the potential of interfering with this possibly to treat vascular disease. Dr. Greg Hundley: Very nice, Carolyn. You know, just more from the world of hydrogen sulfide and endothelial function. Thanks so much. Well, the next paper I have comes to us from Dr. John McEvoy from Johns Hopkins University School of Medicine. So Carolyn, recent clinical guidelines support intensive blood pressure treatment targets. However, observational data suggests that excessive diastolic blood pressure lowering might increase the risk of myocardial infarction. Therefore reflecting, does a J or U-shaped relationship exist when we're following the treatment of diastolic blood pressure? So Carolyn, these authors analyzed 47,407 participants from five cohorts with a median age of 60 years. First to corroborate prior observational analysis, the authors used traditional statistical methods to test the shape of association between diastolic blood pressure and cardiovascular disease. Dr. Carolyn Lam: Okay. So was it J or U? Dr. Greg Hundley: Okay, Carolyn. So interesting, traditional observational analysis of the cohorts suggested a J-shaped association between diastolic blood pressure and myocardial infarction. However by contrast, linear MRI analyses demonstrated an adverse effect of increasing diastolic blood pressure increments on cardiovascular disease outcomes, including myocardial infarction. Furthermore non-linear MRI analyses found no evidence for a J-shaped relationship. Instead confirming that myocardial infarction risk decreases consistently per unit decrease in diastolic blood pressure, even among individuals with low values of baseline diastolic blood pressure. So Carolyn, in answer to you, no, the J or U-shaped curve does not exist. Dr. Carolyn Lam: I suppose depending which way you look at it. Very interesting. Well, let's finish up with what else is in today's issue. There's an AJ update by Dr. Elkin on COVID-19 at one year, the American Heart Association president reflect on the pandemic. A white paper by Dr. Zannad on challenges of cardio kidney composite outcomes in large scale clinical trials. A research letter by Dr. Kass on  the reduced right ventricular sarcomere contractility in HFpEF with severe obesity. Another research letter by Dr. Messas on the feasibility and performance of non-invasive ultrasound therapy in patients with severe symptomatic aortic valve stenosis. A first in human study. Dr. Greg Hundley: Right, Carolyn. So I've got an exchange of letters from Dr. Vazgiourakis  addressing a prior publication entitled Right Heart Dysfunction in COVID-19 Patients: Does Mechanical Ventilation Play an Additional Role? And then finally, an exchange of letters from Drs. Carrizales-Sepúlveda and Topalisky regarding the prior paper, The Spectrum of Cardiac Manifestations in COVID-19. Well, Carolyn, I'm really excited to get to that feature that you explained to us right at the beginning. Very exciting. Dr. Carolyn Lam: So am I. So am I. Thanks, Greg. Dr. Carolyn Lam: Wow. Today's feature discussion could not be more star-studded in my point of view. We are talking about the very, very hot topic of how do we sequence treatments in heart failure with reduced ejection fraction now? A really hot topic because just last year in 2020, we suddenly got a bonanza of positive trials and everybody's grappling with how to put it all together. Dr. Carolyn Lam: Now who better than the two authors I'm going to talk to today, Professor John McMurray from University of Glasgow and Professor Milton Packer from Baylor University Medical Center in Texas. So welcome both. John, Milton, I'm almost tripping over myself to talk about this because this is an amazing perspective piece. Everybody must get your hands on it and even look at the figure while you're listening to this. We're going to divide today's discussion into just three simple questions. Why do we need a new sequencing approach? How in the world do you come up with a new sequencing approach? Based on what? And finally, what is that new approach that you're both proposing? So maybe I'll start off with you, John. What's wrong with what we've been doing? Dr. John McMurray: So Carolyn, I think we've maybe neglected the fact that while we think of, for example, cancer as something that's incredibly urgent to diagnose and to treat as fast as possible, to give the patient all those life-saving therapies, we haven't had the same urgency in our treatment with heart failure. And our existing approaches, as you know, being largely one of start with the first treatment that was ever tested in the trial, up titrate to the pill dose, take your time, then on the second, third and so on. And of course, what that means is that it takes months for patients to be treated with all of the fantastic life-saving options that we have available for them. And we know that that's failing. Dr. John McMurray: We've seen from numerous registries, CHAMP registry in particular springs to mind, where that's simply not happening. It's probably taking too long. It's too complicated for both the doctor and the patient, and we need to change it. And I suppose Milton will tell you his view, but I think my view and I think his as well, was that the SGLT2 inhibitor story really brought this question, I think, to the fore because here is our fourth life-saving drug that if we do things the same way might not get started for six months. And we really felt that we need to rethink what we're doing. Milton, I'm sure, will say whether he agrees with that. But I think that was sort of where the starting point was. Dr. Carolyn Lam: Great. But if I could interject a bit, so now we're talking about that left side of the panel, where in your beautiful article where you're showing, we start with ACE inhibitors and ARBs, and then go on to beta blockers and mineralocorticoid receptor antagonists, and so on. I would love to know, and Milton I'm sure you'll add, is it the sequence that's wrong? Or is it really just the timing? Or the fact that we're just all too lazy? What do you say to people who go, "But that's how the trials were done." Especially because you guys both led those amazing trials of ARNIs and SGLT2 inhibitors. It's just awesome. Dr. Milton Packer: So Carolyn, what's really amazing is that everyone assumes that that's how the trials were done. But two things, one, just because we did things in a certain way, developed things in a certain way, doesn't mean we have to prescribe them in a certain way. I mean, we developed digitalis before all of them and so does that mean we need to use digitalis in everyone? But a lot of the early trials, all the patients were, or most of the patients were, on cardiac glycosides. Dr. Milton Packer: There are four things that we've learned from the large-scale clinical trials. One is the order of drugs does not matter with respect to efficacy. The beta blockers work the same whether people are getting ACE inhibitors or not, MRAs are not effected by background therapy. Neither is neprilysin inhibitors. They work pretty much the same regardless of background therapy, so you don't have to sequence them in the order in which they were developed. Dr. Milton Packer: Two is low doses, low starting doses of these drugs seem to work amazingly well, perhaps surprisingly well. And the third thing is that they work very early. So in a lot of the clinical trials, nearly all the trials that were carried out, there was a meaningful separation of the curves and in effect size in the first 30 days of all of these trials. And in many of the trials, in the first 30 days, patients were still on the starting dose. Hadn't been uptitrated. Dr. Milton Packer: The last point is that these drugs can influence each other's safety profiles. So the result of all of this was for us to rethink what the sequence should be based not on how the drugs were developed, but how they might be most logically used with respect to relative efficacy, safety and ease of use. Dr. John McMurray: So, Carolyn, to go back to your question then is sort of what Milton is saying is that it's a bit of both of the things you asked about. It is about timing, but it's also about the order of the drug. And that last point Milton made is very important about the potential synergies in terms of making it easier to use treatments, but timing is critically important as well. I mean, we do tend in the conventional approach to therapy recommended in the guidelines to perhaps spend too much time trying to reach that target dose, and then doing that before moving on to the second drug. So again as Milton pointed out, if you're getting early benefit from all of these treatments, fundamentally what you want is as many of these treatments started as quickly as possible as you can do safely. And that may be facilitated by some of the synergies between treatments as we, I think, rather provocatively suggested in the new algorithm, might even be possible to start two treatments at once. Dr. Carolyn Lam: Okay. Now I know everybody's really, really wondering what that new algorithm is, but I'm going to lengthen the pain a little bit more because this is critically important. You've already started discussing the how did you come up with an algorithm. It seems a lot of, yeah, very reasonable approaches, but could you give us specific examples of actual scientific interrogation of the data from the trials that you've led, frankly, to show us these points, that maybe support that we can come up with a reasonable new approach? Those points that Milton very rightly put, the treatment benefit of each class is independent. Give us some examples of that. The dose issue, the side effects, how one could help in that too. Could you give us some examples? Dr. Milton Packer: Oh, my God. So let me say that there's so many pieces of evidence and please read the article. We try to summarize as much of them as possible. But in all of the major clinical trials, there was a separation that occurs within 30 days. That's true across every single major trial. Anyone who thinks that the treatment effects of these drugs are delayed, that it takes months to evolve, we're getting statistical significance within two to four weeks across all of the drugs. Dr. Milton Packer: Second is, in many of the trials, for example, COPERNICUS trial with carvedilol, the trials with MRAs, even the trials with ACE inhibitors, during that first 30 days when the curves were separating, patients hadn't been uptitrated. They started on low doses and remained on relatively low doses and the curves were separating. So we knew that the drugs had early effects at low doses, low starting doses. And we also have randomized trials that really tell us that if you go to high doses for some of these drugs, you get a little bit more benefit, but you don't get as much benefit as starting another drug with a different mechanism at a low dose. Dr. Milton Packer: And lastly, we know that some of these drugs actually prevent the side effects of others. There's evidence that neprilysin inhibitors and SGLT2 inhibitors mitigate the hyperkalemia produced by spironolactone and aplerno. So these are just a few examples. Dr. John McMurray: Sorry, Carolyn. To add a couple more, we obviously know that the treatments work independently. We primarily knew that from subgroup analyses, but also from trials like RALES for example, where spironolactone was tested in addition to an ACE inhibitor, but very, very few patients were on a beta blocker. Subsequently we tested different a MRA in patients who were taking both an ACE inhibitor and a beta blocker, and the benefit was essentially the same. And of course, our very first trial with an ACE inhibitor, the CONSENSUS trial, was actually done in a population where more than half of the patients were on a very large dose of an MRA. So you can sort of put all the trials together in a type of jigsaw and figure out that these drugs all clearly work independently. Dr. John McMurray: And then maybe the only other thing I would mention, because it's perhaps relevant to the new algorithm, is that we do have another key trial, which is, a trial I think often forgotten about, the CIBIS III, which was a study that tested whether or not you could start treatment with either a beta blocker or with and ACE inhibitor in patients with HFrEF, showing that you could start with a beta blocker in patients who had not yet received an ACE inhibitor and do that safely and efficaciously. So there's a lot of material out there that you can sort of put together to answer all of these questions. Dr. Carolyn Lam: Great. And now drum roll. Okay. What is the new algorithm? John, you want to introduce it? Or Milton? Up to you. Dr. Milton Packer: John can start. That's fine. Dr. Carolyn Lam: Well, which one, Carolyn? I suppose the one in the Circulation article is a three-step algorithm. It starts with the combination of a beta blocker, based as I mentioned, so there's three plus an SGLT2 inhibitor. So again, thinking about synergies, thinking about tolerability, thinking about size of effect and thinking about repetity of onset of benefit. So I think most of us would agree, beta blockers are incredibly effective treatments, life-saving treatments, reduce the risk of sudden death. We know that you can start a beta blocker safely as first-line therapy. We do know that there may be more intolerance in patients who are volume overloaded. So why not give a treatment that has a modest, initial diarrhetic effect when you're starting the beta blocker? In other words, the SGLT2 inhibitor. SGLT2 inhibitors work extremely quickly. There's no dose up titration needed. So they seem like the perfect combination to start with. Dr. Carolyn Lam: In step two, we suggested moving then to sacubitril valsartan, which in itself is two more drugs combination of an angiotensin receptor blocker and their prolines inhibitor. And then there's the third and final step. We suggested using a mineralocorticoid receptor antagonist. But Milton and I have had a lot of discussion about this. I think we're not saying that all those are necessarily the three steps for all patients. There may be different approaches in different people depending on patient's characteristics. But really the point here was, the provocative statement was we should be able to do this quickly in all patients. And this in fact was an approach to get all four of those drugs started potentially within four weeks. Dr. Milton Packer: So Carolyn, the mantra here, our motto going forwards, is four drugs in four weeks. Dr. Carolyn Lam: Okay. Dr. Milton Packer: An angiotensin receptor blocker, a beta blocker, an MRA, an SGLT2 inhibitor. Four drugs in four weeks. And if you're going to start all four drugs in four weeks, in all honesty, the order probably doesn't matter that much. John and I happen to think that if you have to define a first step, a combination of a beta blocker and an SGLT2 inhibitor simultaneously as step one makes a lot of sense. And then you can follow up with sacubitril valsartan and an MRA. Dr. Milton Packer: But here's the thing that's really important: do not take months to follow up. What we're proposing in this algorithm is you start a beta blocker and an SGLT2 inhibitor on day one, and you then follow through with sacubitril valsartan and an MRA within the next couple of weeks. But here's what's really important and we really need to emphasize this: this is a algorithm that assumes that someone's not on any of these drugs already. And of course, most of these patients are taking some of these drugs already. But the other thing that's really important is that we're also assuming that physicians are being very meticulous about background use of diuretics, so that patients really have to be maintained in a clinically euvolemic state in order to make this algorithm work. Dr. Carolyn Lam: Okay, well, I'm picking myself off the floor because it certainly was provocative. I love it. I love it for that. It's the first time I've ever seen any algorithm start with a beta blocker and SGLT2 inhibitor. You first go, "Where's the ACE and how come the new kid on the block is right on top?" So I really like that because it must challenge our current thinking. In other words, if we just look at the data for what it is, let's see how we could think it over. So salute you for that. But let me just press on a little bit. So four drugs in four weeks. That's really great. Are there any particular patients you may say the ARNIs come on top or the MRAs? Specific situations or...? Dr. Milton Packer: Well, Carolyn, as John has already said, the physicians need to understand the principles, but the application of those principles have to be individualized. So if a patient has a borderline blood pressure, you would probably be well advised to put the MRA before sacubitril valsartan. Depending on renal function, you may decide to advocate one drug a little bit earlier or preferentially compared to another. There are hundreds of individualized nuances, but to get tied up in these is to miss the point of our paper. The point of our paper is that we need to do things quickly... Four drugs, four weeks... And we need to not rely on our historical testing in order to determine the optimal sequence. If you embrace those conclusions, then patients and physicians can individualize their care to the greatest optimal degree. But our current approach, which is a historically-driven algorithm that takes six months to execute, it doesn't work. Dr. John McMurray: Carolyn, we obviously did give a lot of thought to the initial treatments, and we did realize that it would potentially be a surprise to people. But just to reiterate, I don't think there's much debate about the incredible benefits of beta blockers, the size of that benefit. We know that the benefit is apparent within 30 days. So Milton and we had Henry Krum's very nice paper about that in JAMA from the COPERNICUS trial, but we're seen it in the other trials. You know that SGLT2 inhibitors have had early benefit. You think about these two drugs being used in a newly presenting patient with HFrEF, probably don't even need to do any electrolyte monitoring, provided your patients not volume overloaded or recently decompensated. That patient's very unlikely to have any significant intolerance to these two treatments. Dr. John McMurray: They don't, in those sorts of patients, substantially reduced blood pressure in either drug, beta blockers certainly don't affect kidney function. SGLT2 inhibitors have minimal effect on kidney function. If your GFR is relatively normal, you probably don't even need to check it. And of course, there's no effect on potassium. So in terms of getting two treatments onboard quickly that will have a rapid benefit, are likely to be well-tolerated in the type of patient that we just described, and where they might have monitoring necessary is minimal, then this seemed to be the best option. And as Milton said thereafter, it's maybe less important what order you do it in as opposed to the speed with which you do it. And you're right, you would definitely probably tailor this approach according to the patient characteristics, but this was a general starting point to stimulate debate, which it seems to have done. Dr. Milton Packer: So Carolyn, there's something important. If you believe in what we've proposed, then at the end of four weeks, every patient with heart failure and reduced ejection fraction would be on low starting doses or four foundational drugs. Our estimate is if doing that would provide them with a substantial benefit, maybe 70, 75% of the benefit of bringing all of those doses to target doses. And if you can do that, you can do all four drugs at starting doses at four weeks and provide that magnitude of benefit really quickly? That has a big impact on patients. And that has a big impact on public health. Dr. Carolyn Lam: Wow. Just thank you so much for igniting this debate. I wish we could go on forever. I just had to share that when we editors looked at this paper, it did spur a very robust debate. But as you can see, we're publishing it as you've proposed it because we do see where you're coming from. It is very interesting. And I just want to reiterate what you both just said, to listeners out there, remember this is referring to a patient who is compensated. Diuretics are still part of it. Remember that the key message is to get everyone on the four foundational therapies within four weeks. And I just love the way you pushed the boundaries with this. Really, really appreciate it. Milton, you look like you want to say something else. If you'd like closing words, I'd love to... Dr. Milton Packer: We really thank Circulation for having the courage to do this. And please understand, John and I strongly feel that this is the start of a debate. This is the start of a discussion. This algorithm is a proposal to get people to start thinking differently. This is not the final word on the subject by far. We think this is the beginning of a very important discourse that will evolve over the next year or more. And we just wanted to remind people what the clinical trial evidence actually shows about these drugs, because we think it has been frequently misunderstood much to the detriment of patients with heart failure. Dr. Carolyn Lam: Yes. And John, any last words? Dr. John McMurray: I would go back to where I started, Carolyn. In a way, what's important here is to inject a sense of urgency back into the way in which we treat patients with heart failure and reduced ejection fraction. It deserves that sense of urgency, as I mentioned that, for example, cancer does. And also thank you for summarizing, I think, what we tried to get across absolutely accurately. Dr. Carolyn Lam: Okay. So John, Milton, so far I take it. I take your points well, but as a practitioner, what I would do, frankly, is if I have a patient that I'm starting a beta blocker and an SGLT2 inhibitor, I would surely just start an ACE inhibitor perhaps, or ARNI, at the same time. I don't see why I need to delay it. How about that? Even faster? Dr. John McMurray: Okay, well, I'll let Milton answer the faster, but I would say one thing, Carolyn, the new algorithm doesn't mention ACE inhibitors or angiotensin receptor blockers as a monotherapy. Because I think those days are gone. I really do think that we shouldn't go through that cycle of starting a RAS blocker, uptitrating it, then switching to an ARNI because that's a waste of time. You're delaying the introduction of life-saving therapy. And this is the whole point to, again, get that sense of urgency across in implementing all of these treatments as quickly as possible. Dr. Milton Packer: And Carolyn, if you want to go faster, that would be fine. Maybe we shouldn't have proposed four drugs in four weeks. We should have proposed four drugs in four days. But Carolyn, I think that changing the way people think is a gradual process. Perhaps four drugs in four weeks is a good starting point. If everyone feels comfortable with that and understands why we are proposing that, then in another six months or so, Circulation can invite John and I to come back and propose four drugs in four days. But let's see what happens. Dr. Carolyn Lam: Kudos. Thank you so much. Well, thank you once again, John and Milton. That was an incredible discussion. A beautiful paper. Dr. Carolyn Lam: Thank you so much, listeners. I'm sure you enjoyed that as much as I have or probably more. But thank you and please don't forget to tune in again next week. From Greg and I, here's Circulation on the Run. Dr. Greg Hundley This program is copyright of the American Heart Association, 2021.  

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.31.273458v1?rss=1 Authors: Li, B., Veturi, Y., Verma, A., Bradford, Y., Daar, E. S., Gulick, R. M., Riddler, S. A., Robbins, G. K., Lennox, J. L., Haas, D. W., Ritchie, M. D. Abstract: As a type of relatively new methodology, the transcriptome-wide association study (TWAS) has gained interest due to capacity for gene-level association testing. However, the development of TWAS has outpaced statistical evaluation of TWAS gene prioritization performance. Current TWAS methods vary in underlying biological assumptions about tissue specificity of transcriptional regulatory mechanisms. In a previous study from our group, this may have affected whether TWAS methods better identified associations in single tissues versus multiple tissues. We therefore designed simulation analyses to examine how the interplay between particular TWAS methods and tissue specificity of gene expression affects power and type I error rates for gene prioritization. We found that cross-tissue identification of expression quantitative trait loci (eQTLs) improved TWAS power. Single-tissue TWAS (i.e., PrediXcan) had robust power to identify genes expressed in single tissues, but, had high false positive rates for genes that are expressed in multiple tissues. Cross-tissue TWAS (i.e., UTMOST) had overall equal or greater power and controlled type I error rates for genes expressed in multiple tissues. Based on these simulation results, we applied a tissue specificity-aware TWAS (TSA-TWAS) analytic framework to look for gene-based associations with pre-treatment laboratory values from AIDS Clinical Trial Group (ACTG) studies. We replicated several proof-of-concept transcriptionally regulated gene-trait associations, including UGT1A1 (encoding bilirubin uridine diphosphate glucuronosyl transferase enzyme) and total bilirubin levels (p = 3.59e-12), and CETP (cholesteryl ester transfer protein) with high-density lipoprotein cholesterol (p = 4.49e-12). We also identified several novel genes associated with metabolic and virologic traits, as well as pleiotropic genes that linked plasma viral load, absolute basophil count, and/or triglyceride levels. By highlighting the advantages of different TWAS methods, our simulation study promotes a tissue specificity-aware TWAS analytic framework that revealed novel aspects of HIV-related traits. Copy rights belong to original authors. Visit the link for more info

Jane Ferguson:                  Hi there. Welcome to Getting Personal: Omics of the Heart, the podcast from Circulation: Genomic and Precision Medicine. I'm Jane Ferguson, and this is Episode 36 from February 2020.                                                 First up, we have “Identification of Circulating Proteins Associated with Blood Pressure Using Mendelian Randomization” from Sébastien Thériault, Guillaume Paré, and colleagues from McMaster University in Ontario. They set out to assess whether they could identify protein biomarkers of hypertension using a Mendelian randomization approach. They analyzed data from a genome-wide association study of 227 biomarkers which were profiled on a custom Luminex-based platform in over 4,000 diabetic or prediabetic participants of the origin trial.                                                 They constructed genetic predictors of each protein and then used these as instruments for Mendelian randomization. They obtained systolic and diastolic blood pressure measurements in almost 70,000 individuals, in addition to mean arterial pressure and pulse pressure in over 74,000 individuals, all European ancestry with GWAS data, as part of the International Consortium for Blood Pressure.                                                 Out of the 227 biomarkers tested, six of them were significantly associated with blood pressure traits by Mendelian randomization after correction for multiple testing. These included known biomarkers such as NT-proBNP, but also novel associations including urokinase-type plasminogen activator, adrenomedullin, interleukin-16, cellular fibronectin and insulin-like growth factor binding protein-3. They validated all of the associations apart from IL-16 in over 300,000 participants in UK Biobank. They probed associations with other cardiovascular risk markers and found that NT-proBNP associated with large artery atherosclerotic stroke, IGFBP3 associated with diabetes, and CFN associated with body mass index.                                                 This study identified novel biomarkers of blood pressure, which may be causal in hypertension. Further study of the underlying mechanisms is required to understand whether these could be useful therapeutic targets in hypertensive disease.                                                 The next paper comes from Sony Tuteja, Dan Rader, Jay Giri and colleagues from the University of Pennsylvania and it's entitled, “Prospective CYP2C19 Genotyping to Guide Antiplatelet Therapy Following Percutaneous Coronary Intervention: A Pragmatic Randomized Clinical Trial”.                                                 They designed a pharmacode genomic trial to assess effects of CYP2C19 genotyping on antiplatelet therapy following PCI. Because loss of function alleles in CYP2C19 impair the effectiveness of clopidogrel, the team were interested in understanding whether knowledge of genotype status would affect prescribing in a clinical setting. They randomized 504 participants to genotype guided or usual care groups and assessed the rate of prasugrel or ticagrelor prescribing in place of clopidogrel within each arm. As a secondary outcome, they assessed whether prescribers adhere to genotype guided recommendations. Of genotyped individuals, 28% carried loss of function alleles. Within the genotype guided group overall, there was higher use of prasugrel or ticagrelor with these being prescribed to 30% of patients compared with only 21% in the usual care group. Within genotype individuals carrying loss of function alleles, 53% were started on prasugrel or ticagrelor, demonstrating some adherence to genotype guided recommendations.                                                 However, this also meant that 47% of people whose genotype suggested reduced effectiveness were nevertheless prescribed clopidogrel. This study highlights that even when genotype information is available, interventional cardiologists consider clinical factors such as disease presentation and may weight these more highly than genotype information when selecting antiplatelet therapy following PCI.                                                 The next paper is about “Deep Mutational Scan of an SCN5A Voltage Sensor and comes to us from Andrew Glazer, Dan Roden and colleagues from Vanderbilt University Medical Center. In this paper, the team aim to characterize the functional consequences of variants and the S4 voltage sensor of domain IV and the SCN5A gene using a high throughput method that they developed. SCN5A encodes the major voltage gated sodium channel in the heart and variants in SCN5A can cause multiple distinct genetic arrhythmia syndromes, including Brugada syndrome, long QT syndrome, atrial fibrillation, and dilated cardiomyopathy, and have been linked to sudden cardiac death.                                                 Because of this, there's considerable interest in understanding the functional and clinical consequences of different variants, but previous approaches were time consuming and results were often inconclusive with many variants being classified as uncertain significance. This newly developed deep mutational scanning approach allows for simultaneous assessment of the function of thousands of variants, making it much more efficient than low throughput patch clamping. The team assessed the function of 248 variants using a triple drug assay in HEK293T cells expressing each variant and they identified 40 putative gain of function and 33 putative loss of function variants. They successfully validated eight of nine of these by patch clamping data. Their study highlights the effectiveness of this deep mutational scanning approach for investigating variants in the cardiac sodium channel SCN5A gene and suggests that this may also be an effective approach for investigating putative disease variants and other ion channels.                                                 The next article is a research letter from Connor Emdin, Amit Khera, and colleagues from Mass General Hospital in the Broad Institute entitled, “Genome-Wide Polygenic Score and Cardiovascular Outcomes with Evacetrapib in Patients with High-Risk Vascular Disease: A Nested Case-Control Study”. In this study, the team set out to probe the utility of using polygenic risk scores to predict the risk of major adverse cardiovascular events within individuals already known to be at high cardiovascular risk and to assess whether genetic scores can identify individuals who would benefit from the use of a CETP inhibitor such as Evacetrapib. They analyze data from the ACCELERATE trial which had tested Evacetrapib in a high risk population, and they found no effect on the incidents of major adverse cardiovascular events overall. Within a nested case-control sample of individuals experiencing major CVD events versus no events, they applied a polygenic risk score and found that the score predicted major cardiovascular events.                                                 Patients in the highest quintile of the risk score were at 60% higher risk of a major cardiovascular event than patients in the lowest quintile. There was no evidence of any interaction between the genetic risk score and Evacetrapib. These data suggest that genetic risk scores may have utility in identifying individuals at high risk events but may not have utility in identifying individuals who may derive more benefit from CETP inhibition. The next letter concerns “Epigenome-Wide Association Study Identifies a Novel DNA Methylation in Patients with Severe Aortic Valve Stenosis” and comes from Takahito Nasu, Mamoru Satoh, Makoto Sasaki and colleagues from Iwate Medical University in Japan. They were interested in understanding whether differences in DNA methylation could underlie the risk of aortic valve stenosis. They conducted an EWAS or epigenome-wide association study of peripheral blood mononuclear cells or PBMCs from 44 individuals with aortic stenosis and 44 disease free controls.                                                 They collected samples at baseline before a surgical intervention in the individuals with aortic stenosis and collected a follow-up sample one year later. They found that DNA methylation at a site on chromosome eight mapping to the TRIB1, or tribbles homolog one gene, was lower in the aortic stenosis group than in the controls at baseline. They replicated the association in an independent sample of 50 cases and 50 controls. TRIB1 MRNA levels were higher in the aortic stenosis group than the controls. When they looked at methylation status one year after aortic valve replacement or a transcatheter aortic valve implantation in patients with stenosis, they found that DNA methylation had increased in the cases while TRIB1 MRNA decreased. These data suggests that methylation status of TRIB1 and expression of TRIB1 may relate to the disease processes in aortic stenosis such as hemodynamic dysregulation and they can be reversed through surgical intervention. Changes in the methylation status of TRIB1 could be a novel biomarker of response to aortic valve replacement.                                                 The next letter comes from Niels Grote Beverborg, Pim van der Harst, and colleagues from University Medical Center Groningen and is entitled, “Genetically Determined High Levels of Iron Parameters Are Protective for Coronary Artery Disease”. Their study addresses the conflicting hypotheses that high iron status is either deleterious or protective against cardiovascular disease. The team constructed genetic predictors of serum iron status using 11 previously identified snips and tested the genetic association with CAD in UK Biobank data from over 408,000 white participants. Overall, the genetic score for higher iron status was associated with protection against CAD. Ten of the snips suggested individual neutral or protective effects of higher iron status on CAD, while one iron increasing snip was associated with increased risk of disease but this was thought to be likely through an iron independent mechanism. Overall, these data suggest that a genetic predisposition to higher iron status does not increase risk of CAD and is actually protective against disease.                                                 The final letter is entitled, “Confidence Weighting for Robust Automated Measurements of Popliteal Vessel Wall MRI” and comes from Daniel Hippe, Jenq-Neng Hwang, and colleagues from the University of Washington. They were interested in assessing whether images of popliteal artery wall incidentally obtained during knee MRI as part of an osteoarthritis study could be used to study the development and progression of atherosclerosis. They developed an automated deep learning based algorithm to segment and quantify the popliteal artery wall in images obtained over 10 years in over 4,700 individuals. Their approach, which they named FRAPPE, or fully automated and robust analysis technique for popliteal artery evaluation, was able to reduce the average time required for segmentation analysis from four hours to eight minutes per image. They applied weights based on confidence for each segment to automatically improve the accuracy of aggregate measurements such as mean wall thickness or mean lumen area. Their data suggest that this automated method can rapidly generate useful information on atherosclerosis from MRI images obtained as part of other studies. When combined with other data. This approach may facilitate novel discovery in secondary analyses of existing studies in an efficient and cost effective way.                                                 And that's all for issue one of 2020. Come back next time for more of the latest papers from Circulation: Genomic and Precision Medicine. Speaker 2:                           This podcast is copyright American Heart Association 2020.  

Tras haber revisado, en el episodio anterior, la estructura de las lipoproteínas en cuanto a la composición de su superficie y de los lípidos que transportan en su núcleo, aquí nos enfocaremos en la función de estas partículas. Hablaremos sobre la denominada ruta exógena de transporte de los lípidos de la dieta, que tiene como principales actores a los quilomicrones y sus remanentes. También de la ruta endógena para la distribución de colesterol y triglicéridos sintetizados a nivel hepático, en la que las lipoproteínas involucradas son las VLDL, IDL y LDL. Y no podríamos dejar de lado a las HDL y los mecanismos implicados en el transporte reverso del colesterol desde los tejidos periférricos hacie el hígado. Durante esta discusión, mencionaremos a las principales enzimas que tienen la tarea de metabolizar el contenido de estas partículas, como la lipoproteín lipasa, lipasa hepática, CETP y LCAT, abordando paralelamente la manera en que las distintas apolipoproteínas interactúan con ellas. ¿Quieres enterarte semana a semana de las nuevas publicaciones, y acceder a contenido exclusivo? Únete a la lista de correo de Leucocitos isotópicos. Para suscribirte al Podcast de Medicina, estas son las opciones más recomendadas: Apple Podcasts Google Podcasts Spotify Si prefieres explorar más alternativas, haz clic aquí. ¿Te gustó el episodio? Seguro disfrutarás este también: Patogénesis de la aterosclerosis (032) Además, puedes acceder a la lista curada y actualizada de los episodios con mayor aceptación. Este show es para ti. Puedes apoyarlo entrando a Apple Podcasts y dejando allí una calificación positiva. Encuentra las notas de este episodio y dirige a tus amigos a isotopicos.com/060 El objetivo de Leucocitos isotópicos es entretenerte mientras complementas lo que recibes en tu Escuela o Facultad de Medicina. Soy Médico Internista, y comprendo lo demandante que puede ser nuestra Carrera. Por eso decidí crear el Podcast como un curso de Medicina ameno y sin una estructura rígida, que despierte tu interés y curiosidad por esta maravillosa Ciencia. Nunca reemplazará a la Universidad, ni a los libros, pero cumplirá con la misión que lo fundamenta: Ser el lugar donde descansamos de leer, sin dejar de aprender. No olvides que la mejor manera de ayudar a que el proyecto crezca, es contarle a todos de él. ¡Gracias por compartir este episodio con alguien!

CETP on HDL --- Support this podcast: https://anchor.fm/kamesa-anota/support

  Dr. Carolyn Lam: Welcome to Circulation on the Run, your weekly podcast summary and backstage pass to the journal and its editors. We're your cohosts. I'm Dr. Carolyn Lam, Associate Editor from the National Heart Center, and Duke National University of Singapore. Dr. Greg Hundley: And I'm Greg Hundley, associate editor from the Poly Heart Center at VCU health in Richmond, Virginia. Dr. Carolyn Lam: Greg, I'm so excited about the feature paper this week. You know it deals with machine learning. It's such a hot topic now, and this one particularly deals with machine learning and the prediction of the likelihood of an acute myocardial infarction. So everyone's going to want to listen to it. Let's discuss a couple of papers and get to it, shall we? Dr. Greg Hundley: Absolutely Carolyn, would you like to go first? Dr. Carolyn Lam: I sure would. So my first pick is the first study to investigate the overall importance of translational regulatory networks in myocardial fibrosis. This is the study from doctors Rackham and Cook from Duke NUS Medical School here in Singapore. Dr. Carolyn Lam: What they did is they generated nucleotide resolution translatome data during transforming growth factor beta one, or TGF beta one-driven cellular transition of human cardiac fibroblasts to myofibroblasts. So this technique identified the dynamic changes of RNA transcription and translation at several time points during the fibrotic response, revealing transient and early responder genes. Dr. Carolyn Lam: Now, very remarkably about one third of all the changes in gene expression in activated fibroblasts was subject to translational regulation and dynamic variation in the ribosome occupancy, affected protein abundance independent of RNA levels. Ribosome occupancy in the hearts of patients with dilated cardiomyopathy suggest that the same post-transcriptional regulatory network, which was underlying cardiac fibrosis. Now key network hubs included RNA binding proteins such as PUM2 and QKI that worked in concert to regulate the translation of target transcripts in the human disease hearts. Dr. Carolyn Lam: Furthermore, the authors showed that silencing of both PUM2 and QKI inhibited the transition of fibroblasts towards profibrotic myofibroblast in response to TGF beta one. Dr. Greg Hundley: You know, Carolyn, this whole aspect of fibroblasts and how they turn on and turn off, become myofibroblasts, such a hot topic in heart failure. What are the clinical implications of this work? Dr. Carolyn Lam: Yes, I agree. Well, threefold. First, these authors identified previously unappreciated genes under translational control, which could be novel candidates for disease biology and therapeutic targets. Dr. Carolyn Lam: Number two, they found that critical fibrosis factors impacted cellular phenotypes at a protein level only, and hence these cannot be appreciated using single cell, or bulk RNA sequencing approaches. So that was significant. Finally, RNA binding proteins was shown to be central to the fibrotic response and represent unexplored gene expression regulators, and of course potential diagnostic or therapeutic targets. Dr. Greg Hundley: Very nice Carolyn. Well, my next paper is also from the world of basic science, and it comes from Dr. Joseph Hill. Have we ever heard of him? Well of course, he's our Editor in Chief. He's going to discuss, he and his team investigated Polycycstin-1. Well, what is Polycycstin-1? It's a trans membrane protein, originally identified in autosomal dominant polycystic kidney disease, where it regulates the calcium permeate cation channel polycystin-2. So autosomal dominant, polycystic kidney disease patients develop renal failure, hypertension, left ventricular hypertrophy, atrial fibrillation and other cardiovascular disorders. These individuals harbor PC1 loss of function mutations in their cardiomyocytes, but the functional consequences of this are relatively unknown. Dr. Greg Hundley: Now PC1 is ubiquitously expressed in its experimental ablation in cardiomyocyte specific knockout mice reduces contractile function, and in this paper the authors set out to determine the pathophysiologic role of PC1 in these cardiomyocytes. Dr. Carolyn Lam: Huh--very interesting. I liked the way you laid that out. So what did they find? Dr. Greg Hundley: What the investigators identified is that PC1 ablation reduced action potential duration in cardiomyocytes. They decreased calcium transients and therefore myocyte contractility. PC1 deficient cardiomyocytes manifested a reduction in sarcoplasmic reticulum calcium stores due to reduced action potential duration and circa activity, an increase in outward potassium currents decreased action potential durations in cardiomyocytes lacking PC1. PC1 coimmunoprecipitated with a potassium 4.3 channel and modeled PC1 C terminal structure suggested the existence of two docking sites for PC1 within the end terminus of K4.3. Supporting a physical interaction between the cells. Finally, a naturally occurring human mutant PC1 manifested no suppressive effects on this potassium channel activity. Thus, Carolyn, Dr Hill and colleagues' results help uncover a role for PC1 in regulating multiple potassium channels, governing membrane repolarization and alterations in circa that reduce cardiomyocyte contractility. Dr. Carolyn Lam: Oh wow. What a bonanza of really interesting papers in this week. Now my next pick is a secondary analysis of the reveal trial. It hinges on the hypothesis that was generated from prior trials that the clinical response to cholesterol ester transfer protein or CETP inhibitor therapy may differ by ADCY9 genotype. So in the current study, authors Dr. Hopewell and colleagues from Nuffield Department of Population Health, University of Oxford examine the impact of ADCY9 genotype on the response to the CETP inhibitor Anacetrapib within the reveal trial. Dr. Greg Hundley: Tell me, I've forgotten a little bit, but can you remind me a little about what was the reveal trial? Dr. Carolyn Lam: Yes, of course. So the randomized placebo controlled reveal trial actually demonstrated the clinical efficacy of the CETP inhibitor Anacetrapib among more than 30,000 patients with preexisting atherosclerotic vascular disease. Now, in the current study, among more than 19,000 genotyped individuals with European ancestry, 13% had a first major vascular event during four years median follow up. The proportional reductions in the risk of major vascular events did not differ significantly by ADCY9 genotype. Furthermore, the authors showed that there were no associations between the ADCY9 genotype and the proportional reductions in the separate components of major vascular events, or any meaningful differences in lipid response to Anacetrapib. Dr. Carolyn Lam: So in conclusion, the reveal trial being the single largest study to date to evaluate the ADCY9 pharmacogenetic interaction provided no support for the hypothesis that ADCY9 genotype is materially relevant to the clinical effects of the CETP inhibitor Anacetrapib. The ongoing dal-GenE study, however, will provide direct evidence as to whether there's any specific pharmacogenetic interaction with dalcetrapib. Dr. Greg Hundley: Oh, very good. So we've got some results coming from dal-GenE. Dr. Carolyn Lam: Mm. Dr. Greg Hundley: Well, Carolyn, my last selection relates to a paper regarding the incidence of atrial fibrillation among those that exercise, and I mean really exercise. Dr. Carolyn Lam: Ooh. Dr. Greg Hundley: So the paper comes from Dr Nicholas Svedberg from Uppsala University, and studies have revealed a higher incidence of atrial fibrillation among well trained athletes. The authors in this study aim to investigate associations of endurance training with the incidents of atrial fibrillation and stroke, and to establish potential sex differences of such associations in this cohort of endurance trained athletes. They studied all Swedish skiers, so 208,654 that completed one or more races of the 30 to 90 kilometer cross country skiing event called the Vasaloppet from 1989 through 2011, and they had a matched sample of 527,448 non-skiers, and all of the individuals were followed until their first event of either atrial fibrillation or stroke. Dr. Carolyn Lam: Wow. What an interesting and what a big study. So tell us, what are the results and especially were there any sex differences? Dr. Greg Hundley: Well, interesting that you ask about those sex and gender differences. So female skiers had a lower incidence of atrial fibrillation than female non-skiers, independent of their finishing time and the number of races, whereas male skiers had a similar incidence to that of non-skiers. Second, skiers with the highest number of races or fastest finishing times had the highest incidents of the AFib, but skiers of either sex had a lower incidence of stroke than non-skiers independent of the number of races and finishing time. Third, skiers with atrial fibrillation had a higher incidence of stroke than skiers and non-skiers without atrial fibrillation. That's true for both men and women. We would think that. Finally after one had been diagnosed with atrial fibrillation, skiers with atrial fibrillation had a lower incidence of stroke and a lower mortality compared to non-skiers with atrial fibrillation. Dr. Carolyn Lam: Very interesting. Could you sum it up for us? What's the take home? Dr. Greg Hundley: Couple things. One, female endurance athletes appear to be less susceptible to atrial fibrillation than male endurance athletes. Second, both male and female endurance athletes have a lower risk of stroke independent of their fitness level. Third, after the diagnosis of atrial fibrillation, participants in a long distance skiing event with atrial fibrillation had a 27% lower risk of stroke and a 43% lower risk of dying compared to individuals from the general population with the diagnosis of atrial fibrillation. Dr. Greg Hundley: So there's some clinical implications. Although very well trained men have a higher incidence of atrial fibrillation than less trained men, the incidence is on par with that of the general population and not related to a higher incidence of stroke at that group level. This indicates that exercise has very beneficial effects on other risk factors for stroke. Then lastly, atrial fibrillation in well trained individuals should be treated according to our other usual guidelines for the population at whole. Dr. Carolyn Lam: Wow. What a fantastic study to end our little coffee chat on, but it's time to move on to our feature discussion. Dr. Carolyn Lam: Today's feature discussion touches on super-hot topics. First of all, the perennially interesting and hot topic of the prediction of acute myocardial infarction, or should I say the more precise predictions that we can do these days. The second part of the hot topic is machine learning. Oh my goodness. This is creeping into cardiovascular medicine like never before. So I'm so glad to welcome to this discussion corresponding author of the featured paper Professor Nicholas Mills from the University of Edinburgh, as well as our Associate Editor Doctor Deborah Diercks from UT Southwestern. So welcome both, and Nick, if I could start with you, tell us about MI Cubed. Prof Nicholas Mills: First thing to say, it was a major international collaboration, involved researchers from over nine different countries and we got together to develop and test an innovative algorithm that estimates for individual patients the probability when they attend the emergency department with acute chest pain that they may or may not have had a myocardial infarction. Prof Nicholas Mills: Machine learning is a really new area in cardiovascular medicine as you say. Our algorithm called MI Cubed uses a fairly simple algorithm which is a decision tree. It takes into consideration really important patient factors such as age, sex, troponin concentration at presentation, and troponin concentration on subsequent testing, and the change in troponin in between those two tests in order to estimate or calculate the probability of the diagnosis. One of the really interesting aspects of this is it's not just an algorithm for research, it's a clinical decision support tool as well. So what we've done is taken the output from that algorithm and translated it into something that is meaningful for clinicians. We've kept it quite simple. It gives an output between zero and a hundred, which is directly proportional to the likelihood of the patient having a myocardial infarct. We also provide estimated diagnostic metrics. So sensitivities and specificities that relate to that individual patient. It's really going to change the way we think about the interpretation of cardiac troponin in clinical practice. Dr. Carolyn Lam: Indeed, and first audience please, please look up the beautiful figures of this paper. I think it summarizes it all. The algorithm shows you what MI Cubed is and then compares it to the ESC three hour algorithm, one hour algorithm. Then I love the last figure, where you actually show us that very important component that you just said. As a clinical support tool, how it's going to work. So we actually have pictures of your cell phone and showing you the pictures that you're going to get from it. So super cool. Beautiful paper. Dr. Carolyn Lam: Now I just have so much to talk about, first the machine learning bit, always sexy sounding, but a bit scary for clinicians. So I really like the fact that you broke it down to actually say what components go in so that people aren't afraid of this black box. We don't know what's going on. Is there like a set time between samples, or how does this work? Do you need to have it within a certain timing? How does that fall in? Is it a particular type of troponin, what are some of the specs of the model that a practicing clinician needs to know? Prof Nicholas Mills: Well, in order to answer that question, I might explain to you the rationale for developing it. So when you're assessing a patient in the emergency department, we all recognize in our daily practice that patients differ. So interpreting troponin has been challenging. One threshold for all may not be the right way to approach this really important clinical diagnosis. Troponin concentrations differ in men and women. They differ by age, and as a surrogate of the presence of comorbidities. They differ depending on the timing of when you take that sample and when you repeat that measurement, and that has introduced some complexity. So many interesting pathways have been developed for guidelines which try and apply fixed thresholds and fixed time points, and it's pretty tough to deliver in the real world setting of a super busy emergency department. So the premise for developing this algorithm was we wanted something that was really flexible, that recognized that patients are different, they're not all the same. Prof Nicholas Mills: That's why we went for a machine learned approach rather than a more conventional statistical model. So you asked about the specification. You can do your two troponin tests whenever you like. So I had across the 11,000 patients huge variation in the timing of samples, but that is okay for MI Cubed. If you repeat the test within an hour, two hours, three hours, six hours, it still provides the same diagnostic performance. I think that's really important. Prof Nicholas Mills: You also mentioned specification about the assay. This algorithm has been developed using a particular high sensitivity cardiac troponin assay developed by Abbott Diagnostics. It will be effective for other high sensitive troponin assays, but it's unlikely to be as effective using a contemporary assay. So if your hospital uses a contemporary or conventional cardiac troponin assay, this might not be the right algorithm for you. Dr. Carolyn Lam: Great. Thank you for breaking down the issue so beautifully and practically. It really makes me think, oh my goodness, this paper's just far more than about MI. Because you know, natriuretic peptides, you could say the same thing. A prediction of heart failure is the same thing, you know? So the whole approach is novel. Deb, could you please share your thoughts and perspectives on where this is going perhaps? Dr. Deborah Diercks: I think this study is terrific because I think it does, as Dr. Mills stated, reflect reality. We don't draw measures at zero, exactly at zero, and exactly at one and exactly at three, especially in a busy emergency department. So I think it provides flexibility to the physician and provider in using it to be able to interpret values in a world that doesn't fit complete structure like the guidelines are written out. What I find really interesting about this study, and I'd love to hear more about, is how you decided the thresholds of where low risk and high risk were cut at. It mentions by consensus, and I guess I would have loved to have been a fly on the wall to hear how those discussions went, and would love to hear more from you Dr. Mills about that. Prof Nicholas Mills: Fascinating discussions amongst all the investigators on this project as to how we would define that. The first point I would make though is we designed the algorithm to provide a continuous output, a continuous measure of risk. So your MI Cubed score is between zero and a hundred. You don't have to apply a threshold, but we are used to in clinical practice having processes that support our triage of patients, and identifying people as low risk and high risk. Therefore we felt upfront that we should evaluate specific low risk and high risk thresholds. Prof Nicholas Mills: So low-risk, we were completely unanimous on how to define that, and it was based on some really nice work done by emergency physicians in New Zealand. Martin Fan, who's the first author on this paper, surveyed many emergency physicians and asked about their acceptance of risk. They came up with the concept that an algorithm to be considered safe in emergency medicine would be acceptable if the sensitivity was greater than 99% or the negative predictive value was greater than 99.5%. Prof Nicholas Mills: So we agreed up front that we would hold our low risk thresholds to those bars. Those metrics. Where there was less agreement was how you defined high risk. That didn't surprise me hugely. The positive predictive value of troponin is one of the most controversial topics around. Most cardiologists [crosstalk 00:20:52] of troponin has been difficult for them in clinical practice because with the improvements in sensitivity we are seeing lower specificity and lower causative link to value. If I put it into context, just measuring troponin and using the 99 percentile in consecutive patients gives you a positive predictive value of around about 45 to 50% in most healthcare systems for the diagnosis of type one myocardial infarction. Therein lies the problem. So one in every two patients has an abnormal troponin result but doesn't have the condition that we have evidence based treatments for, and whom cardiologists who are often quite simplistic in their approach to the assessment of these patients know how to manage. Prof Nicholas Mills: Every second patient we don't know how to manage, and therefore we wanted an algorithm that would help us identify those patients who can go through our often guideline-based pathways and treatment pathways for acute coronary syndromes more effectively. We eventually agreed that a positive predictive value of 75% would be ideal. So three out of every four patients would have the diagnosis that we knew how to manage and treat. That was our target. We got pretty close to it in our test set. I think the actual positive predictive value at the threshold of around an MI Cubed value of 50 was 72%, so pretty effective. Certainly a lot better than relying on a kind of binary threshold such as the 99 percentile to identify high risk patients. Dr. Deborah Diercks.: Thanks for that great answer. My next question is how do you think MI Cubed is going to integrate, or will it even replace the need for other risk stratification tools that we often use the emergency departments such as TIMI or the heart score? Prof Nicholas Mills: Fabulous question. In this analysis, we haven't specifically compared the performance of MI Cubed with TIMI or heart, so my answer is going to be a little speculative. You can forgive me hopefully. Both those scores were developed prior to the widespread use of high sensitive cardiac troponin tests. I think what we've learned since the introduction of high sensitive cardiac troponin is that we're using this test as a risk stratification tool, and a lot of the power of the MI Cubed algorithm comes from the way that it identifies extremely low risk patients with very low and unchanging cardiac troponin concentrations way below the diagnostic threshold. Prof Nicholas Mills: TIMI and heart simply consider troponin as a binary test, a positive or negative test, and do not take advantage of the real power of the test to restratify patients. All the evidence to date that has compared TIMI and heart with pathways that use high sensitive troponin in this way, both to restratify and diagnose patients show that these risk tools add very little in terms of safety, but do make pathways more conservative. So they identify fewer patients that are lower risk and permit discharge of those patients. Prof Nicholas Mills: So my concern about using an algorithm like MI Cubed with an existing tool like heart is that it will undermine much of the effectiveness of this tool which identifies around about two thirds of patients as low risk. If you were to combine that with a heart score, you would reduce the effectiveness. I don't think you get a gain in performance, but further research is required to do a head to head comparison with these sorts of traditional restratification tools. Dr. Carolyn Lam: I'm so grateful for this discussion, both Nick and Deb. In fact, I was about to ask what are the next steps and I think Nick you just articulated it. Deb, I want to leave the final words to you. Do you have anything else to add? Dr. Deborah Diercks: I think this study represents a real change in how we can practice medicine, where we can actually take our biomarkers that actually have really strong value and utilize them in a manner that is pragmatic. It can actually introduce and take full advantage of them, and so I think this is a great opportunity for us to rethink our usual approach, which frankly, especially for troponin has really been very binary and very static. Thank you so much Dr Mills for the innovation and the willingness to look into this area. Dr. Carolyn Lam: Thank you so much. This paper is like a sneak peak into the future of what we'll be practicing medicine like. Well, audience, you heard it right here on Circulation on the Run. Don't forget to tune in again next week. This program is copyright American Heart Association 2019.

****Note the audio quality is not ideal for this episode, please bear with us as the content itself is well worth the listen*******  While Garrett is off galavanting in Europe Tamara scores the ultimate interview with Kymberly Stephens, MS, LPC, CETP, CCTP-II. Kym's accolades are too many to count, and Tamara and Garrett can both attest to the power of brainspotting, as they have both been fortunate enough to have one-on-one sessions with Kymberly.   Tamara interviews Kym about how brainspotting was discovered, how it works in the brain, and the benefits of it for trauma treatment and other uses. They even throw in some dream interpretation technique, just because she couldn't help herself! If you are anywhere near Fayetteville, Arkansas, we strongly recommend looking her up.  Please send us an email at liferecoveredpodcast@gmail.com  Please subscribe, rate, and review as this helps the podcast tremendously. Books mentioned on this episode:   The Body Keeps the score: by Bessel van Der Kolk

Courtney Gendron, Lyndon Rickards and Jeremy Vore share the Top 3 changes to the 2019 edition of the Certified Employee Training Program (CETP). If you have a comment about the training content, be sure to email safety@propane.com.

In this five-part series, Thomas Dayspring, M.D., FACP, FNLA, a world-renowned expert in lipidology, and one of Peter's most important clinical mentors, shares his wealth of knowledge on the subject of lipids. In Part III, Peter and Tom dig into HDL, why "reverse cholesterol transport" is a lot more nuanced than what most of us are taught, lipid transport, apolipoproteins, and more. In addition, this episode highlights the complexity of HDL and a discussion about the CETP inhibitor trials. We discuss: Reverse cholesterol transport [1:40]; Lipid transportation, apolipoproteins, VLDL, IDL, and LDL particles [11:00]; Remnant lipoproteins and apoC-III [16:45]; Particles having sex: lipid exchange [28:00]; Cholesteryl Ester Transfer Protein (CETP) and CETP inhibitors [40:45]; 2006 CETP inhibitor trial: torcetrapib (Pfizer) [54:45]; 2012 CETP inhibitor trial: dalcetrapib (Hoffmann–La Roche) [56:15]; 2017 CETP inhibitor trials: evacetrapib (Eli Lilly) and anacetrapib (Merck) [58:00]; and More. Learn more at www.PeterAttiaMD.com Connect with Peter on Facebook | Twitter | Instagram.

Dr Carolyn Lam:                Welcome to Circulation on the Run, your weekly podcast summary and backstage pass to the journal and its editors. I'm Dr Carolyn Lam, associate editor from the National Heart Center, and Duke National University of Singapore. Will artificial intelligence replace the human echocardiographer? Aha, well to find out the answer, you have to wait for the incredibly exciting discussion of today's feature paper coming right up after these summaries.                                                 The clinical benefits of the cholesterol ester transfer protein, or CETP inhibitor dalcetrapib depends on adenylate cyclase type 9, or ADCY9 genotype. However, what are the underlying mechanism responsible for the interactions between ADCY9 and CETP activity? In the first paper from today's journal first author Dr Rautureau, corresponding author Dr Tardif from Montreal Heart Institute, and colleagues used a mouse atherosclerosis model inactivated for ADCY9 and demonstrated that loss of ADCY9 protected from atherosclerosis and was associated with improved endothelial function, but only in the absence of CETP. ADCY9 in activation increased weight gain, adipose tissue volume, and feed efficiency, but only in the absence of CETP.                                                 This mouse model reproduced the interactions between ADCY9 and CETP activity observed in patients, and offers new mechanistic insights for the importance of ADCY9 in determining the responses to CETP inhibition. For example, the dal-GenE clinical trial is currently testing prospectively whether patients with coronary disease and the favorable ADCY9 genotype will benefit from dalcetrapib.                                                 The next study addresses the controversy around the cardioprotective effects of Omega-3 polyunsaturated fatty acids, and uncovers signaling pathways associated with eicosapentaenoic acid, or EPA supplementation that may mediate protective effects in atherosclerosis. First author Dr Laguna-Fernandez, corresponding author Dr Bäck from Karolinska Institute, and their colleagues showed that EPA supplementation significantly attenuated atherosclerotic lesion growth. They performed a systematic plasma lipidomic analysis and identified that 18 monohydroxy eicosapentaenoic acid was a central molecule formed during EPA supplementation. 18 monohydroxy eicosapentaenoic acid was a precursor for the plural resolving lipid mediator called resolvent E1.                                                 In the present study, a resolve in E1 was shown to regulate critical atherosclerosis related functions in macrophages through its downstream signaling receptor to transfuse protective effects in atherosclerosis.                                                 Are there racial differences and long-term outcomes among survivors of in-hospital cardiac arrest? In the next paper first and corresponding officer Dr Chen from University of Michigan and her colleagues performed a longitudinal study of patients more than 65 years of age who had an in-hospital cardiac arrest and survived until hospital discharge between 2000 and 2011 from the National Get With The Guidelines Resuscitation Registry whose data could be linked to Medicare claims data. They found that compared with white survivors of in-hospital cardiac arrest, black survivors had a more than 10% lower absolute rate of long-term survival after hospital discharge. This translated to a 28% lower relative likelihood of living to one year, and a 33% lower relative likelihood of living to five years after hospital discharge for black versus white survivors.                                                 Nearly one-third of the racial difference in one-year survival was dependent on measured patient factors. Only a small proportion was explained by racial differences in hospital care, and approximately one-half was the result of differences in care after discharge, or unmeasured confounding. Thus, further investigation is warranted to understand to what degree unmeasured, but modifiable factors, such as post-discharge care may account for the unexplained disparities.                                                 The next study provides insights into a novel mechanism of atherogenesis that involves protease-activated receptor 2, a major receptor of activated factor 10, which is expressed in both vascular cells and leukocytes. Co-first authors Dr Hara and Phuong, corresponding author Dr Fukuda from Tokushima University Graduate School of Biomedical Sciences, and their colleagues showed that in ApoE-Deficient deficient mice, protease-activated receptor 2 signaling activated macrophages and promoted vascular inflammation, increasing atherosclerosis.                                                 Furthermore, they showed that in humans, plasma-activated factor 10 levels positively correlated with the severity of coronary artery disease, suggesting that the signaling pathway may also participate in atherogenesis in humans. Thus, the protease-activated receptor 2 signaling pathway may provide a novel mechanism of atherogenesis and serve as a potential therapeutic target in atherosclerosis.                                                 The next paper tells us that biomarkers may help to predict specific causes of death in patients with atrial fibrillation. First and corresponding author Dr Sharma and colleagues from Duke Clinical Research Institute evaluated the role of biomarkers in prognosticating specific causes of death among patients with atrial fibrillation and cardiovascular risk factors in the ARISTOTLE trial.                                                 They looked at the following biomarkers: high sensitivity troponin T, growth differentiating factor 15, N-terminal pro-B-type natriuretic peptide, and interleukin 6. They found that sudden cardiac death was the most commonly adjudicated cause of cardiovascular death, followed by heart failure and stroke or systemic embolism deaths. Biomarkers were some of the strongest predictors of cause-specific death, and may improve the ability to discriminate among patients' risks for different causes of death.                                                 How do the complement and coagulation systems interact in cardiovascular disease? Well in the final original paper this week, first author Dr Sauter, corresponding author Dr Langer from Eberhard Karls University Tübingen, and their colleagues used several in vitro, ex vivo, and in vivo approaches as well as different genetic mouse models to identify the anaphylatoxin receptor C3AR and its corresponding ligand C3A as platelet activators that acted via intra -platelet signaling, and resulted in activated platelet fibrinogen receptor GP2B3A. This in turn mediated intravascular thrombosis, stroke, and myocardial infarction. This paper, therefore, identifies a novel point of intersection between the innate immunity and thrombosis with relevance for the thrombolic disease of stroke and myocardial infarction.                                                 That wraps up with week's summary. Now for our featured discussion.                                                 Can we teach a machine to read echocardiograms? Well today's feature paper is going to be all about that. I am so excited to have with us the corresponding author of an amazing, and I think, landmark paper, Dr Rahul Deo from the One Brave Idea Science Innovation Center and Brigham and Women's Hospital in Boston, as well as our associate editor Dr Victoria Delgado from Leiden University Medical Center in The Netherlands. Now let me set the scene here. We know that echocardiography is one of the most common investigations that we do in cardiology, and in fact even outside of cardiology, and it is hands down the most accessible, convenient tool to image the heart.                                                 Now let's set this up by remembering that echocardiograms are performed with machines, but led by echocardiologists like me. Now this is really scary Rahul because I think your paper is trying to say ... Are you trying to put people like me out of business? Dr Rahul Deo:                    Definitely not. I think what I'm hoping to do is actually two things. One of them is, despite the fact that it's an accessible and safe tool, because it needs people like us, it's probably not used as often as ideally it could be. So part of our hope was to democratize echocardiography by being able to take out some of the expenses from the process so that we can hopefully get more simpler studies done at an earlier stage in the disease process. Because in many ways, at least from my experiences being an attending, it feels like if we could just have gotten to these patients earlier we may have been able to start therapy that could've changed the disease course, but our system can't really afford to do huge numbers of echoes on asymptomatic patients. Really we were trying to find some way of facilitating this by at least helping out on trying to quantify some of the simple things that we do with echocardiography. Dr Carolyn Lam:                I love that phrase, democratizing echo. And you're absolutely right, if we could put it in the hands of non-experts and help them interpret them, we could really lead to detecting disease earlier, and so on and so forth. Wow. But everyone's wondering, how in the world do you go about doing that? Dr Rahul Deo:                    One of the things that's really been amazing in these last five years or so is that the field of computer vision, so the field by which computers are trained to mimic humans in terms of visualizing, recognizing, identifying images, has really advanced, and incredibly rapidly. And one of the reasons for that is that the video game type of computing system, the same things that go into Playstations and such, have resulted in much, much more rapid computing. And that's allowed us to train more complex models.                                                 So that's one of the things that's changed, and also, it's just much easier to get our hands-on training data. So machines can be trained to do things, but they need lots of examples. And the harder the task, the more examples they need. So the widespread availability of digital data has made that easier, though I would say that it wasn't that easy to get our hands on enough echocardiography data to be able to train. But in general, almost any task where there's enough data has been solved on the computer vision side. So this has really been an exciting advance in these last few years. So we thought we could very well just used these same technologies on a clinical problem. Dr Carolyn Lam:                Okay, but Rahul what are you talking about here? Like the machine's actually going to recognize different views, or make automated measurements? That's the cool thing, frankly, that you've written about because we know that the machines can already kind of do EF, ejection fraction, but you're talking about something way bigger. So tell us about that. Dr Rahul Deo:                    Yeah, so there are many cute examples in the popular press about machines being able to recognize the differences between cats and dogs, or some breeds of dogs. And so if you think about things that way, it really shouldn't be that much more difficult to imagine recognizing between different views, which probably are much more dramatically different than different breeds of dogs. So you could really just take the same models, or the same approaches, give enough examples, label them, and then say figure out what the differences are.                                                 And I think one of the challenges with these systems is they're often black boxes. They can't tell us exactly what it is that they're using, but when it comes to something like recognizing whether something is an apical four chamber view or a parasternal long axis view, we actually don't care that much as to how it is that the computer gets there. We just wanted them to do it accurately, and that's one of the places for some of these computer vision models. It's a field broadly called deep learning, and it's just great at achieving complex tasks.                                                 So, once you recognize views, then the other thing that computers have been shown to be able to do is recognize specific objects within an image. For example, you could give an entire football field and you could find a single player within it. You could recognize where the players are, where the ball is, where the grass is. So computers can distinguish all those things too. And then once you know where something is, you can trace it and you can measure it. So in that sense it's very similar to what a human reader would do, it's just broken down into individual steps, and each one of those needs to be trained. Dr Carolyn Lam:                You put that so simply so that everyone could understand that. That's so cool. You mentioned, though, accuracy. I could imagine that a machine would likely interpret one image the same way again and again, and that addresses something that we really struggle with in echo doesn't it? Because, frankly, one reader against another, we always know. Ejection fraction has got a plus minus seven or something, and then even within the same reader you could read the same thing and say something one day, and say something the other. So this is more than just automating it, is it? Dr Rahul Deo:                    Yeah, so it's certainly making it more consistent, and the other thing that we were able to do, I mean once you can teach it to identify and traces the contours of the heart in one image you can have it do it in every single image within the video, and every single video within the study. So now, I mean it's quite painful. I know this from my own experience in terms of tracing these things, so a typical reader can't trace 150, 200, 300, 500 different hearts, that's not going to happen. So instead, they'll sort of sift through manually, pick one or two, and if there's variability from one part of the study to the other, that really won't be captured.                                                 And in this case, the computer will very happily do exactly what you ask it to do, which is to repeat the same thing again and again and again, and then be able to average over that, capture variability. So that's one of the tasks that is much more easy to imagine, setting a computer who won't talk back to you and won't resist and won't refuse to actually taking on the mundane aspect of just getting many, many, many more measurements. And that could happen not only in a single study, but also could happen more frequently. So you could imagine that, again, there's just not that resistance that's coming from having to have an individual do these things. Dr Carolyn Lam:                Oh, my goodness, and not only does he not ... well he, machine, not say no, I mean they don't need to take time off or weekends off. We could get immediate reports directly. Oh my goodness. Victoria I have to bring you in on this. We knew as editors when we found this paper that this is something we just have to publish in Circulation that's going to be groundbreaking. Could you tell us a little bit more about what you think the implications of this is? Victoria Delgado:              I think that this is a very important paper because it's a very large study and it's sets, I would say, three important questions that we deal every day in clinical practice. One is how to reduce burden in very busy echo labs by facilitating the reporting of the echoes and the interpretation of the echoes. Second: to have an accurate measurement and quantification of the images that we are acquiring, and third: this is recognition of the pattern.                                                 And I think that this very important, particularly in primary care because, for example in Europe here, echocardiography is not really in the primary care and the patients are being referred to secondary level hospitals or third level hospitals. That means that the waiting days sometimes is too long. If we train the general practitioners, for example, to do simple echocardiograms with the handheld systems which are also the technologies that are coming and are really available in your iPhone, for example, on your phone, you can get an echocardiographic evaluation of a patient that comes to a general practitioner.                                                 And if you don't have too much knowledge on interpretation, these tools that can have recognition of the pattern of the disease can trace a red flag and say, okay this patient may have this disease or may have this problem, you should consider sending or referring this patient to us at Leiden Hospital where he's going to have a regular check-up and a complete echocardiogram. That could lead to less burden in very busy labs and only refer the patients in a timely manner to the centers when they have to be referred, when the others can wait of can be referred much later.                                                 I think that that's important, and next two technologies that are coming now and it will be very important, some groundbreaking technologies. One is the handheld systems, the ones that you can have in your phone, the ones that you can have in your tablet for example. And the other one is going to be the artificial intelligence to, if not diagnose completely, at least to recognize the pattern that there is a pathology where we need to focus, and we need to act earlier. Dr Rahul Deo:                    I think that one place we would like to see this used is in a primary care setting where you have individuals who have risk factors that we know would be risk factors, for example, for let's say heart failure with preserved ejection fraction. But really, my experience in that phase of clinical practice is there's a lot of resistance from patients to get on the medications. So hypertension is, at that point, often, I just got worked up because I had a hard time finding parking, and so on, and so on, where there's just a natural resistance.                                                 So if you could imagine having objective measures describing, let's say how their left atrium is doing at that point, how it looks the next year, what the change in therapy is doing, all these things, you actually can bring in that quantification at a low enough cost that makes it actually practical, then that would be one place we could imagine motivating or intensifying therapies on the basis of something like this.                                                 And I think one area we have to admit we didn't solve is we haven't solved the ability to facilitate getting the data in the first place. We do know that there are these focused workshops around trying to get some simple views, and more and more of our internal medicine residents are able to get some of these, but we can't dismiss that this is still an important challenge in terms of being able to get the images. What we want to do is say, well you can get some images and we can help you interpret them and quantify in an effort to try to motivate therapies being initiated or intensified in a way that's sometimes difficult to do in the current system. Dr Carolyn Lam:                So, Rahul and Victoria, you both mentioned that one of the key aspects is the acquisition of the echo. Not just the machine that does it, but also who takes the images that will then be automatically analyzed. So, Rahul, do you think that sometimes you're going to invent something that will replace even the acquisition, or maybe even simplify it so that we may not need Doppler anymore? Dr Rahul Deo:                    One of the things that we thought about was, we wanted to limit ourselves to views that might be easier to acquire, in part because we wanted to reduce the complexity of the study and yet still try to capture as much information as possible. And getting back to the first part of your question, you could imagine that recognizing a view is not that different from recognizing that a view is 10 degrees off from where it should be. You could imagine training a computer to do just that very same thing too. It could recognize a slightly off axis apical four chamber view and guide you into correctly positioning the probe, and you could even imagine a robotic system that does this and just takes the person out of it all together. In part because a very skilled sonographer can quickly look at something and say, oh I just need to tilt my wrist this way and move it this way. I was always humbled by that because I never could quite do that myself.                                                 But in the same way, and in the way, that's happening is that an image is recognized, and then the reference image is held in one's brain, and then they just know from experience what needs to be done to turn one into the other. But that very well-oiled machine could very well be taught to do that exact same thing too. Dr Carolyn Lam:                Oh wow. That is just totally amazing. I know the listeners are being blown away by this just as I am. Let me just end by asking for any last words, Victoria and Rahul, of the clinical application of this. When are we going to have this primetime? What do you think? Victoria Delgado:              I think that this is coming. This is one, for example, of the first studies showing the feasibility of this technology. In terms of accuracy, probably we need improvement, but that depends very much on the quality of the echocardiographic data that we obtain. And in the future, I think that we are going to rely more and more on this technology, and we will have the expert view for those cases that are ambiguous or where the technology has limitations. But in terms of accuracy, for example, I can imagine one of the clinical scenarios that we face in everyday clinical practice is the evaluation of the effect of the treatment in heart failure patients for ejection fraction, and in patients, for example, treated with chemotherapy to see changes in ejection fraction.                                                 That, if we do it manually as we do now, we know that we have limitations in terms of the own viability of the observer. If you leave it for artificial intelligence, maybe that viability may be reduced, and you may be better in terms of adjusting the medication if needed. Because you removed completely what would be the individual viability. So these are the fields that probably I see more and more application of this technology in order to improve the reproducibility of the measurements and accuracy. But yeah, for that we need probably very good image quality, and I see in echocardiography we always tend to say, yeah the image quality is not that good. I'm sure that echocardiography can give you much more than just using through the echocardiography. You can use contrast, you can use many other techniques in order to improve the image quality. And artificial intelligence, the better the image quality is, probably the better it's going to be as well, the accuracy of the measurements and the recognition of disease. Dr Carolyn Lam:                Wow, and Rahul? Dr Rahul Deo:                    I completely agree with Victoria. I think that we're going to have to be clever about where we incorporate something like this into the current clinical workflow. You have to choose your problem carefully, you have to understand it. Any system like this is going to make some mistakes. To figure out how to minimize the impact of those mistakes, and at the same time add benefit and potentially enable things that wouldn't even be done. So I think that the fun stuff is yet to come here in terms of really incorporating this in a way that can really change clinical practice.                                                 I want to add one thing that I really haven't mentioned. And we, at this point, really just focused on trying to mimic the stuff that we're already doing. Part of the motivation of this work is to try to potentially see things that we can't even see right now and try to potentially predict onset of disease or early latent forms of something that would really be difficult to detect by the human eye. And we've seen examples of that in some of the other fields around radiology, and I think that's going to be a place that would be augmenting beyond what we're even doing currently.                                                 But of course, the challenge is that the system has to be interpretable enough that we understand what it is that it's seeing, because otherwise I'm sure we'll be reluctant to embrace something clinically that we don't understand. Dr Carolyn Lam:                You've been listening to Circulation on the Run. Don't forget to tune in again next week.  

Jane:                                     Hi, everyone. Welcome to Episode 18 of Getting Personal: Omics of the Heart. I'm Jane Ferguson, and this podcast is brought to you by the Circulation: Genomic and Precision Medicine Journal and the American Heart Association Counsel on Genomic and Precision Medicine. It is July 2018, which means that the best possible place to be listening to this episode is at the beach, but failing that I can also recommend listening on planes, during your commute, while exercising or while drinking a nice cup of tea.                                                 So before I get into the papers we published this month, I want to ask for your help. If you're listening to this right now, hi, that means you, we're a year and a half into podcasting and I would love to know what content you like and where we could improve things. We have a poll up on Twitter this week, and I would really appreciate your input. If you're listening to this a little bit later and miss the active voting part of the poll, you can still leave suggestions.                                                 Okay, so what I would like you to do right now is to go to Twitter. You can find us as Circ_Gen and locate the poll. If you don't already follow us on Twitter, go do that now too. We want you to let us know what content we should focus on and what is most useful to you, so go ahead and pick your favorites from the options and also please reply or tweet at us with other thoughts and suggestions.                                                 Options include giving summaries of the recent articles like I'm about to do later this episode, conducting interviews with authors of recently published papers, interviews with people working in cardiovascular genomics, broader topics. For example, to get their insight on career paths and lessons learned along the way.                                                 And something we have not done yet on the podcast but are considering, would be to record podcasts that focus on particular topics in genomics and precision medicine. These could give some background on an emerging field or technology and we could talk to experts who are leading particular innovations in the field. So, if that sounds good to you, let me know! If you're not on Twitter, I don't want to exclude you, so you can email me at jane.f.ferguson@vanderbilt.edu and give me your thoughts that way. I'm looking forward to hearing from you.                                                 Okay, so on to the July 2018 issue of Circ.: Genomic and Precision Medicine. First up is a PhWAS from Abrahim Rao, Eric Ingelsson, and colleagues from Stanford. The discovery of the PCSK9 gene as a regulator of cholesterol levels has led to a new avenue of LDL lowering therapies through PCSK9 inhibition. However, some studies suggest that long term use of PCSK9 inhibitors could have adverse consequences. Because of the long follow-up time required, it will take many more years to address this question through clinical studies. However, genetic approaches offer a fast and convenient alternative to address the issue.                                                 In this paper, entitled: "Large Scale  Phenome-Wide Association Study of PCSK9 Variants Demonstrates Protection Against Ischemic Stroke," the authors use genetic and phenotype data from over 300,000 individuals in the UK BioBank to address whether genetic loss of function variants in PCSK9 are associated with phenotypes including coronary heart disease, stroke, type II diabetes, cataracts, heart failure, atrial fibrillation, epilepsy, and cognitive function.                                                 The missense variant RS11591147 was associated with protection against coronary heart disease and ischemic stroke. This SNP also associated with type II diabetes after adjustment for lipid medication status. Overall, this study recapitulated the associations between PCSK9 and coronary disease, and revealed an association with stroke.                                                 Previous studies suggested use of LDL lowering therapies may increase risk of cataracts, epilepsy, and cognitive dysfunction, but there was no evidence of association in this study. Overall, this study provides some reassurance that the primary effect of PCSK9 is on lipids and lipid related diseases, and that any effects on other phenotypes appear to be modest at best. While a PhWAS can't recapitulate a clinical trial, what this study indicates is that PCSK9 inhibition is an effective strategy for CVD prevention, which may confer protection against ischemic stroke and does not appear to convey increased risk for cognitive side effects.                                                 Next up we have a manuscript form Jason Cowan, Ray Hershberger, and colleagues from Ohio State University College of Medicine. Their paper, "Multigenic Disease and Bilineal Inheritance in Dilated Cardiomyopathy Is Illustrated in Non-segregating LMNA Pedigrees," explored pedigrees of apparent LMNA related cardiomyopathy identifying family members who manifested disease, despite not carrying the purported causal LMNA variant. Of 19 pedigrees studies, six of them had family members with dilated cardiomyopathy who did not carry the family's LMNA mutation. In five of those six pedigrees, the authors identified at least one additional rare variant in a known DCM gene that was a plausible candidate for disease causation.                                                 Presence of additional variants was associated with more severe disease phenotype in those individuals. Overall, what this study tells us is that in DCM, there is evidence for multi-gene causality and bilineal inheritance may be more common than previously suspected. Future larger studies should consider multi-genic causes and will be required to fully understand the genetic architecture of DCM.                                                 Yukiko Nakano, Yasuki Kihara, and colleagues from Hiroshima University published a manuscript detailing how HCN4 gene polymorphisms are associated with tachycardia inducted cardiomyopathy in patients with atrial fibrillation. Tachycardia induced cardiomyopathy is common in subjects with atrial fibrillation, but the pathophysiology is poorly understood. Recent studies have implicated the cardiac hyperpolarization activated cyclic nucleotide gated channel gene, or HCN4, in atrial fibrillation and ventricular function.                                                 In this paper, the authors enrolled almost 3,000 Japanese subjects with atrial fibrillation, both with and without tachycardia-induced cardiomyopathy, as well as non-AF controls. They compared frequency of variants in HCN4 in AF subjects with or without tachycardia-induced cardiomyopathy, and found a SNP, RS7164883, that may be a novel marker of tachycardia-induced cardiomyopathy in atrial fibrillation.                                                 Xinyu Yang, Fuli Yu, and coauthors from Tianjin University were interested in finding causal genes for intracranial aneurysms, and report their results in a manuscript entitled, "Rho Guanine Nucleotide Exchange Factor ARHGEF17 Is a Risk Gene for Intracranial Aneurysms." They sequenced the genomes of 20 Chinese intracranial aneurysm patients to search for potentially deleterious, rare, and low frequency variants. They found a coding variant in the ARHGEF17 gene which was associated with associated with increased risk in the discovery sample, and which they replicated in a sample of Japanese IA and in a larger Chinese sample.                                                 They expanded this to other published studies, including individuals of European-American and French-Canadian origin and found a significantly increased mutation burden in ARHGEF17 in IA patients across all samples. They were interested in further functional characterization of this gene and found that Zebra fish ARHGEF17 was highly expressed in blood vessels in the brain. They used morpholinos to knock down ARHGEF17 in Zebra fish, and found that ARHGEF17 deficient Zebra fish developed endothelial lesions on cerebral blood vessels, and showed evidence of bleeding consistent with defects in the vessel. This study implicates ARHGEF17 as a cerebro-vascular disease gene which may impact disease risk through effects on endothelial function and blood vessel stability.                                                 Sumeet Khetarpal, Paul Babb, Dan Rader, Ben Voight, and colleagues from the University of Pennsylvania used targeted resequencing to look at determinants of extreme HDL cholesterol in their aptly titled manuscript, "Multiplexed Targeted Resequencing Identifies Coding and Regulatory Variation Underlying Phenotypic Extremes of HDL Cholesterol in Humans." Stay tuned because we're gonna hear more about this paper from the first author Dr. Sumeet Khetarpal later this episode.                                                 Rounding out this issue we have a Perspective article from Chris Haggerty, Cynthia James, and coauthors from Geisinger and Johns Hopkins Medical Center entitled, "Managing Secondary Genomic Findings Associated With Arrhythmogenic Right Ventricular Cardiomyopathy: Case Studies and Proposal for Clinical Surveillance." In this paper the authors discuss the challenges for returning findings from clinical sequencing for arrhythmogenic right ventricular cardiomyopathy, presenting case studies exemplifying these challenges. They also propose a management approach for returning clinical genomic findings, and discuss new innovations in the light of precision medicine.                                                 We also published a review article by Pradeep Natarajan, Siddhartha Jaiswal, and Sekar Kathiresan from MGH on "Clonal Hematopoiesis Somatic Mutations in Blood Cells and Atherosclerosis", which discusses recent advances in our knowledge on the role of somatic mutations in cardiovascular disease risk.                                                 Finally, we have an update on some pharmacogenomics research into CYP2C19 Genotype-Guided Antiplatelet Therapy by Craig Lee and colleagues which we published a few months ago. Dr. Lee was also featured on Podcast episode 15 in April of this year.                                                 Jernice Aw and colleagues from Khoo Teck Puat Hospital, Singapore shared from complimentary data from their sample of 247 Asian subjects which found the risk for major adverse cardiovascular events was over 30-fold greater for poor metabolizers, as defined by CYP2C19 genotype on clopidogrel, as compared to those with no loss of function allele.                                                 You can read that letter and the response from Dr. Lee and colleagues online now. And, as usual, all of the original research articles come with an editorial to help give some more background and perspective to each paper. Go to circgenetics.ahajournals.org to find all the papers and to access video summaries and more.                                                 Our interview is with Dr. Sumeet Khetarpal who recently completed his MD-PhD training at the University of Pennsylvania, and is currently a resident in Internal Medicine at Massachusets General Hospital. Sumeet kindly took some time out from his busy residency schedule to talk to me about his recently published paper, and to explain how molecular inversion probe target capture actually works.                                                 So I am here with Dr. Sumeet Khetarpal who is co-first author on a manuscript entitled, "Multiplexed Targeted Resequencing Identifies Coding and Regulatory Variation Underlying Phenotypic Extremes of High-Density Lipoprotein Cholesterol in Humans."                                                 Welcome Sumeet, thanks for taking the time to talk to me. Dr. Khetarpal:                    Thank you so much Dr. Ferguson, it's really a pleasure to talk to you today. Jane:                                     Before we get started, maybe you could give a brief introduction on yourself and then how you started working on this paper. Dr. Khetarpal:                    Sure, so this work actually was a collaboration that came out at the University of Pennsylvania that I was involved with through my PhD thesis lab, my mentor was Dan Rader, and also a lab that is a somewhat newer lab at Penn, Benjamin Voight's lab which is a strong sort of computational genomic lab.                                                 This work actually highlights the fun of collaborating within your institution. We had, for some time, been interested in developing a way to sequence candidate genes. Both known genes and also new genes that have come out of genome-wide association studies that underlie the extremes of HDL cholesterol, namely very high cholesterol versus low HDL cholesterol. We've been looking for a cost-effective and scalable way to do this.                                                 Independently, Ben, who is very interested in capturing the non-coding genome, was interested in developing a method to better understand the non-coding variation, both common and rare variation that may be present at all of these new loci that have come out for complex traits such as HDL.                                                 We, at some Penn event several years ago, were talking about our common interest and Ben had actually identified this work that had come out of J. Shendure's lab at the University of Washington. A paper by the first author, Brian O'Rouke, in Science in 2012 in which they had developed an approach that involved molecular inversion probes, or MIPs, to capture regions of the genome related to target the gene that they were interested in studying for autism-spectrum disorders.                                                 They had applied this largely to coding regions of, I think, almost 50 genes and almost 2,500 patients with the feedback to do deep, targeted sequencing. So our thought was, well, we could try to apply this approach and adapt it to capture non-coding regions, and also see if we can expand the utility of this approach to study the phenotypic extremes of a complex trait such as HDL cholesterol. Jane:                                     Yeah, that's really cool. I love how you saw this method in a totally different application and then realized that there was expertise at Penn that you could bring together to apply this in a different way.                                                 I'd love to hear more about this MIP, the molecular inversion probe. How does it work? How difficult is it to actually do? Is it very different from normal library preparation for sequencing or is it something that's actually relatively easy to apply? Dr. Khetarpal:                    These MIP probes are oligonucleotide probes that capture your region of interest by flanking them and capturing by gap filling. There's a method to capture parts of the genome in a library-free way. They do ultimately involve barcoding the way traditional library-based target capture does and then deep sequencing.                                                 But the most impressive feature about them is just that they're very scalable. I think in the original paper by O'Rouke and colleagues they were able to sequence their set of genes and their set of samples at about a sample preparation cost of $1 per sample, and we were actually able to do about the same for our study.                                                 The main utility of the approach is just the economic scalability, and the ability to customize your panel to capture several regions of the genome that are adjacent to each other. Jane:                                     Right, so how many genes or regions can you multiplex at the same time? Is it just one prep, like you just design all of your oligos, you put them all together in one reaction, or are you doing separate reactions for each region? Dr. Khetarpal:                    We're actually doing all of our oligos together. In our case, I think it ended up being around the order of almost 600 oligos together to capture our ultimately 50kB of genomic territory that we wanted to capture. Really, our study was kind of a pilot experiment where we picked a few genes or regions of high interest to us, both known genes that effect HDL and also those that have been implicated in genome-wide association studies that were of high interest to our labs.                                                 I think that this approach could actually be expanded to capture much more genomic territory in a single capture reaction. We sort of touched the surface probably of what we could do. Jane:                                     Wow, that's cool! And then for sequencing it, I guess it's really just a function of how many samples you wanna multiplex and how much you want to sequence from each region. So I suppose the way you did it, you had about 50kB and then you had over 1,500 participants and you were able to do those on a single HiSeq run, right? Dr. Khetarpal:                    Right. Jane:                                     So I suppose if you'd done more genetic regions, you would've had fewer people and vice versa so you can balance that out depending on if you're having more samples or more genomic regions to sequence. Dr. Khetarpal:                    Exactly, in certain ways the design of our experiment we had a limited sample size that did afford us some luxury in terms of knowing that we would have deep coverage of the region that we were targeting. I think that's always a critical question in sort of targeted or just sequencing in general. The balance between the number of regions that you want to sequence and the number of samples you want to sequence is going to dictate what your sequencing depth with be. Jane:                                     Right, okay so I guess if we go on to what you actually found, how'd you pick this? You picked seven regions which encompasses eight candidate genes for HDL, so how did you select those? Dr. Khetarpal:                    The population that we were studying, the samples we were looking to sequence were largely individuals which fall into two bins if you will. One was extremely high HDL cholesterol which we're defining as the greater than the 95th percentile, but really there was a range within that population that spanned individuals with probably greater than the 99th percentile of HDL.                                                 We were hoping as a proof of principle effort to identify variation in genes that were known causes of high HDL cholesterol in prior studies of Mendelian genes for HDL. So genes such as LIP gene which encodes endothelial lipase or CETP or SCARB1, these 3 genes are, at this point, well-known genes that loss of function mutations are associated with extremely high HDL. We thought that capturing some of those genes would potentially both provide a level of validation for the approach, hypothesizing that individuals with high HDL would be enriched with these genes, but also may allow us to find new variants in these genes or also non-coding variants which has not previously been studied before.                                                 Some of the genes came out from that line of thinking, then some of the other genes happened to be genes that in the Rader laboratory we had a vested interest in understanding the genetic variation that might link the genes to HDL, which may not have necessarily come out before.                                                 For example, the gene GALNT2 is one of the first g-loss implicated novel genes for HDL, novel as in the earliest g-loss study for plasma lipids had identified that gene as associated with HDL but it never had come out before as being so. Our laboratory was very interested in better understanding the genetic relationship between genes such as GALNT2 and several of the others such as CCDC92 and ZNF664 with HDL.                                                 It ended up being a hodge-podge or a sampling of genes that had at some level been implicated with HDL, but really it's just a proof of principle that this method could work for both identifying variation in known genes and also less studied ones. Jane:                                     You validated the MIP genotyping by exome genotyping, and then saw concordance of over 90%, is that lower than you were expecting? Was it about what you were expecting based on these two different methods of genotyping? Dr. Khetarpal:                    Yes, I think we were expecting somewhere on the order of 90 plus percent. It's hard to know why we just hit that, we likely would've benefited from being able to genotype all of the individuals by the exome chip that we had sequenced as well, where we were able to validate in about two-thirds of those individuals.                                                 It's hard to know exactly what the cause of the about 10% discordance rate might be, whether it's just in certain samples the genotyping quality was perhaps on the border of being valid or the sequencing quality. Jane:                                     Right, I'm wondering sort of with the MIP, what's the gold standard? Is the XM chip genotyping still the gold standard and the MIP maybe is more error-prone, or perhaps the other way around? Or is it you can't tell at this point which is the true genotype and which is an error potentially for those discordant ones? Dr. Khetarpal:                    Certainly whenever there's a new sequencing methodology that is proposed I think it's critical to have some sort of validation. We happened to cover regions that would span the genome enough that we had XM chip genotyping in a large subset, that that might be the best approach. But if you had a limited number of regions or variance that you were interested in one could imagine also doing Sanger sequencing as the tried and tested validation approach. Of course it becomes not so scalable at a certain point.                                                 Certainly we would say that the MIPs, while the method has been developed and expanded by the Shendure lab, our hope is that through our studies maybe it will be applied further. It's still very much a new approach and so validation is key. Jane:                                     Very important. What do you think was the most exciting finding that came out of this, after you analyzed the data, what were you most excited about seeing? Dr. Khetarpal:                    The critical finding for us, which I think implies the utility of the approach, was just the validation of four of the loci that we had studied. Validation in our cohort of known genome-wide significant associations for HDL that had been published previously in almost 200,000 individuals in terms of sample size, in our experiment involving just about 1,500 people we were able to find consistent associations of those same variants that segregated with low versus high HDL. Directionally consistent with the large genome-wide association studies.                                                 I think the value of this finding is really just to emphasize the utility of the case control design in these phenotypic extremes, in addition to the overarching goal of our study, which was in a way that perhaps provides the most validation of the approach in terms of concordance with prior known studies. Jane:                                     So if somebody was listening to this and was trying to decide should they use MIP for a study they have in mind, should they use another technique? Based on your experience, what would you recommend? Dr. Khetarpal:                    I think in our current stage it's a very exciting time because we're just seeing whole genome sequencing really take off and being used at scale to ask critical questions about non-coding variation as it relates to both disease and complex traits. I don't think we're quite there yet with being able to apply that approach in a cost effective manner. The ability to annotate and analyze that data is still at it's infancy. The utility of the MIPs is that it provides a very cheap alternative.                                                 I can say from my experiences actually doing the capture and preparation from sample to sequencer stage that it's a very easy to use methodology that is very fast and cheap. That if one is really interested in a handful, or more than a handful, of candidate genes and their non-coding regions as it relates to a trait or disease of interest, it may not be the era for going full on with whole genome sequencing, especially at the current cost. That's where I think the MIPs really come in to be very useful. Jane:                                     It sounds great, is there anything else that you'd like to mention? Dr. Khetarpal:                    Just to say that we recognize it's a relatively small study as our pioneer approach with this method but that the Rader lab and Voight labs are actively pursuing larger applications of this to study, not only HDL, but other complex traits, such as diabetes, in much larger populations. I can't overemphasize how easy of a method it is to apply, but also that I think a bigger take home of this study for me as a very recent graduate student working in a very collaborative institution the ability of two laboratories to come together with different sets of expertise to try to tackle a problem that I think goes beyond the individual science. For any human geneticist how to find the variation you're interested in and not break the bank is kind of at the core of what we do, and so I think it was very fun to be part of this collaboration and our hope is that the outcome of it is a method that can be useful for many people, both in our field and beyond. Jane:                                     I think it's great and I'm hoping this will inspire a lot of other people to try this method and see if it can work for them. So, congratulations on the study, it's really nice work. Dr. Khetarpal:    Thank you so much!                                                                                                                                       Jane:                                     That's all I have for you for July, thanks for listening. Send me your thoughts on the podcast via Twitter or email, or leave us a review in Itunes. I look forward to talking to you next month.  

Jane Ferguson:                 Hi, everyone. Welcome to Getting Personal: Omics of the Heart. This is podcast episode 16 from May 2018. I'm Jane Ferguson from Vanderbilt University Medical Center and this podcast is brought to you by Circulation Genomic and Precision Medicine and the AHA Council on Genomic and Precision Medicine. Jane Ferguson:                 This month we talked to Dr. Caitrin McDonough from the University of Florida. We briefly mentioned her paper in last month's episode Genetic Variants Influencing Plasma Renin Activity in Hypertensive Patients From the PEAR Study, but we wanted to go into it in more depth this month. Caitrin shared with us that this manuscript actually resulted from student course work and was a collaborative effort between students and instructors. The manuscript highlights has successful as approach can be both in increasing student engagement and as an effective way to do high quality research. You can hear her talk more about her innovative approach to student learning and the study findings later in this episode. Jane Ferguson:                 Of course, we have a great lineup of papers in Circulation Genomic and Precision Medicine this month. First up, a paper entitled, "SCN5A Variant Functional Perturbation and Clinical Presentation Variants of a Certain Significance" by Brett Kroncke, Andrew Glazer, and Dan M. Roden and colleagues from Vanderbilt University Medical Center. They were interested in investigating the functional significance of variants in the cardiac sodium channel in particular to see if they could explain why some variant carriers present with cardiac arrhythmias while others remain asymptomatic. Through a comprehensive literature search, they identified 1712 SCN5A variants and characterized the carriers by disease presentation. Variants associated with disease were more likely to fall in transmembrane domains consistent with the importance of these domains for channel function. Jane Ferguson:                 Using American College of Medical Genetics Criteria for variant classification, they found that variants classified as more pathogenic were also more penetrant. Penetrance was also associated with electrophysiological parameters. This approach highlights how modeling the penetrance of different variants can help define disease risk for individuals who carry potentially pathogenic variants. Jane Ferguson:                 Next we have a paper from Vincenzo Macri, Jennifer Brody, Patrick Ellinor, Nona Sotoodehnia and colleagues from the University of Washington and Massachusets General Hospital. This is also related to sodium channels and the paper is entitled, "Common Coding Variants in SCN10A Are Associated With the Nav1.8 Late Current and Cardiac Conduction". They were interested in SCN10A and sequenced this gene in over 3600 individuals from the CHARGE consortium to identify variants associated with cardiac conduction. They were able to replicate associations between variants and PR and the QRS intervals in a sample of almost 21,000 individuals from the CHARGE Exome sample. They identified several missense variants have clustered into distinct haplotypes and they showed that these haplotypes were associated with late sodium current. Jane Ferguson:                 Continuing the cardiac conduction theme, Honghuang Lin,  Aaron Isaacs and colleagues published a manuscript entitled, "Common and Rare Coding Genetic Variation Underlying the Electrocardiographic PR Interval". They conducted a meta-analyses of PR interval in over 93000 individuals which included over 9000 individuals of African ancestry. They identified 31 loci, 11 of which have not been reported before. We see SCN5A come up again as a gene of interest in this study but their analyses also implicated a novel locus, MYH6. Jane Ferguson:                 Next up moving from the heart to the vasculature, Janne Pott, Markus Scholz and colleagues from the University of Leipzig published a manuscript entitled, "Genetic Regulation of PCSK9 Plasma Levels and Its Impact on Atherosclerotic Vascular Disease Phenotypes". They were interested in whether circulating PCSK9 can be used as a diagnostic or predictive biomarker. To address this, they conducted a GWAS of plasma PCSK9 in over 3000 individuals from the LIFE-Heart study. They found that several independent variants within the PCSK9 gene were associated with plasma PCSK9 as well as some suggestive variants in another gene locus FBXL18. They used Mendelian randomization to probe causality and the data suggest that PCSK9 variants have a causal role in the presence and severity of atherosclerosis. Jane Ferguson:                 Moving on to another biomarker, Lisanne Blauw, Ko Willems van Dijk and colleagues from the  Einthoven Laboratory for Experimental Vascular Medicine report on CETP in their manuscript Cholesteryl Ester Transfer Protein Concentration A Genome-Wide Association Study Followed by Mendelian Randomization on Coronary Artery Disease. They aimed to assess potential causal effects of circulating CDP on cardiovascular disease through GWAS and Mendelian randomization. Jane Ferguson:                 In a study of over 4000 individuals from the Netherlands Epidemiology of Obesity Study, they identified three variants in CTP that associated with plasma levels of CETP and explained over 16% in the total variation in CDP levels. Genetically predicted in CETP was associated with reduced HDL and LDL cholesterol suggesting that CETP may be causally associated with coronary disease. Jane Ferguson:                 Rounding out the table of contents we also have a clinical case perspective from Nosheen Reza, Anjali The Importance of Timely Genetic Evaluation in family members in cases of cardiac disfunction and cardiomyopathy. We have a report from Adrianna Vlachos, Jeffrey Lipton and colleagues on the Diamond Blackfan Anemia Registry and we have a clinical case from Yukihiro Saito, Hiroshi Ito and colleagues on TRP and poor mutations in patients with ventricular non-compaction and cardiac conduction disease. Jane Ferguson:                 To read all of these papers and the accompanying commentaries, log on to circgenetics.aha.journals.org and if you're a visual learner or you need a work related excuse to spend time on YouTube, you can also access video summaries of all our articles from the CircGen website or directly from our YouTube channel Circulation Journal. Lastly, follow us on Twitter at circ_gen or on Facebook to get new content directly in your feed. Jane Ferguson:                 I'm joined today by Caitrin McDonough from the University of Florida and Caitrin is an Assistant Professor in the Department of Pharmacotherapy and Translational research in the College of Pharmacy and she's the first author on a recently published manuscript entitled, "Genetic Variants Influencing Plasma Renin Activity in Hypertensive Patients From the PEAR Study". This was published in the April 2018 issue of Circulation Genomic and Precision Medicine. Welcome, Caitrin. Caitrin M.:                           Thank you. Jane Ferguson:                 For listeners who haven't had a chance to read the paper yet, I wonder could you give us a brief overview of what prompted you to do this study? Caitrin M.:                           Sure so this looks at plasma renin activity and just initially a GWAS but it was done in a hypertensive population from the pharmogenomic evaluation of antihypertensive responses study. Particularly, since our group here at the University of Florida is more interested in pharmacogenomics we wanted to address plasma renin since it can influence blood pressure response to antihypertensive medication particularly if you use it as something to predict but also to correlate it with that as there have been also prior data from our group that shows if you have different levels of plasma renin that would predict if you would respond better to certain types of antihypertensive medications such as a beta-blocker or a diuretic. Caitrin M.:                           We used both a GWAS approach as well as a prioritization through blood pressure response to focus in on signals and then furthered by using prioritization using data from RNA seq and looking at eQTLs and then finally looking at more of just a traditional net replication of the original plasma renin activity signal. Caitrin M.:                           Overall, one of the interesting things and why we were initially doing this study was really in connection with a graduate course that myself and another faculty member here who's also an author on the paper, Yan Gong [inaudible 00:09:12]. We often have the students analyze data from the PEAR study as we have a lot of data from that study and it helped us analyze additional papers but we didn't necessarily know if this was going to be an interesting phenotype but through that course work which turned out that it really did have some interesting signals so we wanted to follow up more on. Jane Ferguson:                 Yeah, I love that approach so I think that's a really smart way to do it. To actually get your students to analyze your data and get them really involved in the process. How much then did the students ... how much were then they able to get involved when it started transpiring that their results would actually be something that could be put together for a manuscript? Caitrin M.:                           Overall, they are fairly involved. During the course work, what we usually do is give them just directly types data since a lot of them have not done this type of genetic analysis before and we split it up where each student gets about four to five chromosomes of data and then different phenotypes in the different race groups as we have both whites and African Americans. They get a certain race group, certain number of chromosomes and so they're able to conduct the analysis just using the Uplink software which is fairly user-friendly and straightforward. Then they get experience making Manhattan plots and using LocusZoom. Caitrin M.:                           After they have the basic techniques, then we teach them how to start following up top signals and determine what is a good signal. They're looking at the LD or SNP function or possibly gene function or looking at their genotype, phenotype relationships and making sure that it's not just one person who's driving the whole signal. Then selecting what top reasons and top SNPs may need a follow up. That part they all do there in the class and learn more of the basics. Caitrin M.:                           After the class, the students who want to continue to participate we get together and redistribute data where they would then move on to working on the imputed data sets and we teach them how to do that. Then we give them ... operate it somewhat similar to a consortia level meta-analysis type thing. I write up an analysis plan, each student does some part of the analysis. They have to bring it all back to me. I sort through it. We meet and go through it. Then we set our next steps to follow up. Then different students get different SNPs to investigate the function of or different subanalyses to do. Caitrin M.:                           One of our graduate students who is on this particular project, her dissertation project was very focused on our RNA seq data so that was how we were able to bring in the eQTL analysis using the RNA seq data as she had done a lot of the groundwork with that already. In one of our discussions that was one of the ways that we were able to incorporate the prioritization since she was intimately familiar with that data set. Jane Ferguson:                 Yeah and I think that's great. I can imagine that, that's a much more compelling way for students to learn about how to analyze data when they see the natural follow through. Do you find that some of the students maybe get more excited about research or are more likely to pursue future research opportunities by having had this hands on experience with the publication process and completing a project really did to this very end? Caitrin M.:                           They do, yeah. I see some of the students that end up sticking with it more are the students who I work more closely with and see more closely some of the students who are from other departments still stay involved but sometimes don't stay quite as involved. But, all of them really do continue to follow up and ask if they can still help or if there's anything they need to do until we get it to publication which is really nice. Jane Ferguson:                 Yeah, right. I think that's fantastic and I'm sure every study has its challenges. I'm interested what were the challenges you encountered in doing this study and which one of them may be unique to the way you have a lot of different people analyzing different aspects of the data versus the regular challenges that would come up in a study like this. Caitrin M.:                           Yeah so some of it I think is just keeping everyone on track and keeping it organized, making sure I think some of our challenges with this study was just me making I think on a lot of other studies, while I had certainly hands on the data it was more of an oversight rule for some pieces of it and just making sure everything looked the way that I thought it did, double checking. Some of it I think the teaching aspect of it just making sure everything was also done correctly and then keeping everything organized made the study a little bit more challenging. Caitrin M.:                           I think part of it too was with the PEAR study, it is a very rich data set. Determining what we wanted for our prioritization scheme and how to work through the different types of data sets that we had and put it all together as initially we just assign each student a different piece and we had a vague plan but it was a little bit more tricky as to work through how it was all coming together then when everyone came back together since a lot of people were doing as opposed to just one person doing it. Jane Ferguson:                 Right, so yeah and I think you're touching on the part that all of us have when we're writing papers that you sometimes end up with a lot of data at the beginning, you're trying to sift through it and then sometimes at a certain point you see something and you're like, "Okay, yes. This is interesting." Then you start following it up. Jane Ferguson:                 I wonder at what point did that happen? I suppose you probably ... You ran the GWAS for plasma renin activity and then find a number of suggested SNPs that were significant you associated but then ... Describe your strategy and you did so the second screening stuff to look at the pharmacological aspect defining [crosstalk 00:15:12]? Caitrin M.:                           Yeah, our initial plan going in was the first two steps, to do the GWAS for plasma renin activity and then to do the prioritization through blood pressure responses. I was very familiar with what our lab was familiar with but then after we got there, I think we were then troubled with what we did next and where to go. When we decided to bring in the RNA seq data, I think that was when it really started coming together as our top signal, the SNN-TXNDC11 gene region really stuck out then and it showed up. That seemed like a much stronger signal and it gave us a little bit more focus and also brought it much more of a functional aspect where we would maybe start to believe that signal more. That I think was really when we did that more of a turning point for the study and helped us focus more on where then to go with the results. Jane Ferguson:                 As far as the data you had I think over 700 people for your GWAS. Then you had a pretty large number of ... Was it the same subjects or different subjects where you also had the RNA seq data to do the QTL analysis? Caitrin M.:                           The same subject so not everyone has RNA seq. We have RNA seq data on 50 individuals and they were selected from whites and at the extremes of the blood pressure response so that it has a slightly interesting selection process. It's the main data analysis there was a best responder, worse responder to thiazide diuretics. Caitrin M.:                           When we do the eQTL analysis, we aren't always sure what we're going to get since we're missing the middle of blood pressure response. But, when we're just looking strictly at eQTL analysis sometimes we get lucky and sometimes it looks weird. Jane Ferguson:                 In your case as well you had the added issue of subjects were randomized to drug treatment so it was some where responders were ... I guess some people got the drug that worked for them and some people did not get the drug that worked for them. Caitrin M.:                           Yeah. Jane Ferguson:                 Did you I guess were incorporating both groups so good responders to either and some of that was because of their gene. They got the right drug for their genotype. Caitrin M.:                           Exactly, yeah. Jane Ferguson:                 It's good and then you were able to replicate this. After you were able to prioritize your gene region based on the GWAS and the drug response and the eQTL data, you actually ended up being able to go to a second sample to replicate the association right? Caitrin M.:                           Yes, it was in a lot of the same investigators, we have a second study PEAR-2, which has a very similar design to the PEAR study but used different drugs but also collected baseline plasma renin activity. We were able to use that phenotype again. We did have slight differences in GWAS to imputation panels at that point in time for when we were conducting this study so we ended up using a proxy to replicate but we did see the same signal in the second population which was very nice to see. Jane Ferguson:                 What is known about this gene region or either of these two genes? Caitrin M.:                           Overall, that was I think one of our harder points when we started trying to make the connections back to our phenotype. This was one of the areas where we did also have help from our students and the students as that was part of their initial training where they really looked to see what function was of various different genes and how to follow them up. That was one area where they came in, was to help look up some of the function of these ... there have been some connections with the various genes, the other phenotypes and with SNN and to atherosclerosis and other inflammatory cytokines such as TNF-alpha. Then there have been data also from [inaudible 00:19:37] that really show that there is an eQTL in this region that which supports what we saw in our own data. However, there really wasn't any direct connections with renin and the renin angiotensin aldosterone system and blood pressure regulation that we could find in the literature. Caitrin M.:                           We're not exactly sure how it connects but based off of our functional data and levels of evidence and then we saw some of that in publicly available data, we're still very interested in the region. Jane Ferguson:                 I think the data is compelling enough that it looks like you've identified the new region that probably is the mechanistically related that will require a whole bunch of basic mechanistic research to figure out what exactly the genes in this region are doing and how this ultimately connect back to blood pressure and response to drug therapy. Caitrin M.:                           Exactly. Jane Ferguson:                 I could see this ... I mean obviously there's a whole lot of potential functional work there and then probably also the clinical work, I wonder what you think about how this would affect any pharmacogenetic therapeutic ... You know at present I think you can look at plasma renin activity and use that as a predictor of drug response to help guide therapies. Would you think that a genotype guided therapy may end up being more effective than the plasma renin activity measurement in this case? Caitrin M.:                           In this case since this is so connected with a phenotype that you could use with plasma renin, I think if you're able to draw a plasma renin you may just want to do that. I think our overall goal would be if someone had preexisting genetic data and you weren't wanting to do an additional test or if you're contemplating response to a lot of different drugs that perhaps you could use a genetic data. One of the issues that was brought up on review and that are a lot of group considers quite a lot is that we have a lot of signals and that our group has certainly published a lot in this area and there's a lot of signals that we have to a lot of different drugs and how do you incorporate all of them together, is there overlap between them or where do they all fall? Caitrin M.:                           That is certainly something that we're still working on as more I think ultimate goal would be more to delve more of a SNP score or gene score and some type of risk score that would help you determine what drug you would best respond to. We've done that a little bit in some of our prior publications but we haven't yet taken all of our data together and help to build something that would if you had a lot of data on an individual and various different alleles at various different genes, how that would respond. Caitrin M.:                           Overall, when we look at blood pressure response as a pharmacogenetic signal, certainly we see larger affect sizes than you would in disease genetics but we're not seeing affect sizes like you would with more of an adverse drug event. We're in between there and we're often times it's not necessarily just going to be one SNP or one gene that would tell your whole story but a combination of quite a few of them. Jane Ferguson:                 I wonder are there more similar stories like this from the same data set? You know you've been through this process from start to finish and building in the functional work and do you think that next year's class will be able to do this again with the same data set? That maybe pick one of the next priority candidate down the list and maybe find another interesting story like this? Caitrin M.:                           Yeah, so we actually just finished our class this year and they looked at potassium. We just got done grading final papers and submitting grades so we will over the summer be working with them a little bit more. I think some of our new graduate students too are starting to work on trying to make more connections between a lot of our different phenotypes and as you start to layer those together what it exactly means for a patient or implementation perspective. Jane Ferguson:                 Yeah, interesting. We'll have to look for that story whenever you guys get done with it. Otherwise, are you planning on following up this specific SNP region in any other way or any other studies? Where's next for you guys overall? Caitrin M.:                           I think one of the things we would like to do is look at this more in PEAR-2. We really just brought the PEAR-2 data set in here as replication of the top region in that last stage but we have that data set and we can certainly look at that data set. Caitrin M.:                           The other thing that I would like to do is as we started this project in conjunction with the class that was a couple of years ago at this point in time, we used [TAP/MAP 3 00:24:35] imputed data since that was what we had in the lab and what we were using at that point in time. At this point in time, we have now imputed both PEAR and PEAR-2 2000 genomes phase three data. It'd be interesting to see if we are able to see any additional signals or if these regions become stronger or exactly what would happen using a more imputation panel that has more coverage and where we would have the same panel between both PEAR and PEAR-2. Jane Ferguson:                 Right because you may or may not have identified the causal SNPs in the previous access but- Caitrin M.:                           Yeah. Jane Ferguson:                 -yeah so it'd be nice to see if you can actually get that out. That potentially could end being a drugable target maybe suitable for something more specific but who knows. Is there anything else that we haven't covered yet that you'd like to mention? Caitrin M.:                           Overall, I think that just this type of model of utilizing more of a real world analysis and data in a class project really certainly engages our students a lot and I think they all enjoy actually being able to work with data that came out of this study and have a lot more hands-on experience and really project-based analysis experience. We've been very happy with this model and have used it multiple times. We have an HDL paper, the renin paper, our glucose response paper and now we're working on the potassium project. It's been a good model for us here with our pharmacogenomics class. Jane Ferguson:                 Yeah, I mean I think it's a really smart and intuitive way to think about education. It's mutually beneficial it sounds like, so it's helping you guys get your data analyzed. It's really helping the students learn so I think it's a win-win situation. I think it's a model that a lot of other people would really be interested in adopting. Caitrin M.:                           Yeah. Jane Ferguson:                 Okay well thanks so much for talking to me and talking about your model and your research. It's been great. Caitrin M.:                           Yes, thank you very much for having me. Jane Ferguson:                 That's it from us for May. Thank you for listening and come back for more next month.  

On the September 13th, 2017 episode of the YourLIVINGBrand.live show we speak with Collin Bissoondatt, Canada's Employee Termination Specialist. The name alone begs you to tune in! The show is all about what makes you different and valuable to your audience. Want to know more about us, please visit www.yourbrandmarketing.com.

A summary of Cornell University's Dr. Andrew Grimson's "The Messenger's Tale: Decoding the 3'UTR" presentation at UCONN starts the show. Highlights from Cleveland Clinic's Medical Innovation Summit are innovations in cancer therapy. New research supports the idea that specific groups of microbes living in our gut could be protective against obesity - and that their abundance is influenced by our genes. A new study finds people with a variant in the CETP gene may survive the longest.

Nuclear magnetic resonance spectroscopy (NMR) provides robust readouts of many metabolic parameters in one experiment. However, identification of clinically relevant markers in H-1 NMR spectra is a major challenge. Association of NMR-derived quantities with genetic variants can uncover biologically relevant metabolic traits. Using NMR data of plasma samples from 1,757 individuals from the KORA study together with 655,658 genetic variants, we show that ratios between NMR intensities at two chemical shift positions can provide informative and robust biomarkers. We report seven loci of genetic association with NMR-derived traits (APOA1, CETP, CPS1, GCKR, FADS1, LIPC, PYROXD2) and characterize these traits biochemically using mass spectrometry. These ratios may now be used in clinical studies.

Interview with Steven E. Nissen, MD, author of Effects of the CETP Inhibitor Evacetrapib Administered as Monotherapy or in Combination With Statins on HDL and LDL Cholesterol: A Randomized Controlled Trial

Introduction: Coronary artery disease progression after primary coronary artery bypass grafting may, beside classical atherosclerosis risk factors, be depending on genetic predisposition. Methods: We investigated 192 CABG patients (18% female, age: 60.9 +/- 7.4 years). Clinically cardiac adverse events were defined as need for reoperation (n = 88; 46%), reintervention (n = 58; 30%), or angina (n = 89; 46%). Mean follow-up time measured 10.1 +/- 5.1 years. Gene polymorphisms (ApoE, NOS3, LIPC, CETP, SERPINE-1, Prothrombin) were investigated separately and combined (gene risk profile). Results: Among classical risk factors, arterial hypertension and hypercholesterinemia significantly influenced CAD progression. Single ApoE, NOS3 and LIPC polymorphisms provided limited information. Patients missing the most common ApoE epsilon 3 allele (5,2%), showed recurrent symptoms (p = 0,077) and had more frequently reintervention (p = 0,001). NOS3 a allele was associated with a significant increase for reintervention (p = 0,041) and recurrent symptoms (p = 0,042). Homozygous LIPC patients had a higher reoperation rate (p = 0.049). A gene risk profile enabled us to discriminate between faster and slower occurrence of cardiac adverse events (p = 0.0012). Conclusion: Single APOE, LIPC and NOS3 polymorphisms permitted limited prognosis of cardiac adverse events in patients after CABG. Risk profile, in contrast, allowed for risk stratification.

Audio Journal of Cardiovascular Medicine, November 6th, 2007 Reporting from: American Heart Association Scientific Sessions, 4-7 November, 2007, Orlando, Florida Torcetrapib in Patients at High Risk for Coronary Events: ILLUMINATE Trial Latest Results PHILIP BARTER, Heart Research Institute, Sydney, Australia COMMENT: GORDON TOMASELLI, Johns Hopkins University, Baltmimore REFERENCE: Late Breaking Clinical Trials Session 2 A randomized double-blind study involving over 15,000 patients at high cardiovascular risk which looked at the new agent torcetrapib (an inhibitor of cholesteryl ester transfer protein, CETP) resulted in higher mortality in the experimental arm. The study compared torcetrapib plus atorvastatin with atorvatstin alone. Inhibition of CETP increases HDL levels and reduces LDL levels and should combat atherosclerosis. Sarah Maxwell spoke with Philip Barter who presented data on the ILLUMINATE trial at the American Heart Association meeting in Orlando.

Guest: Robert S. Rosenson, MD Host: Matthew J. Sorrentino, MD, FACC, FASH Will novel HDL cholesterol therapies reduce cardiovascular risk? Dr. Robert Rosenson will discuss the hope for CETP inhibitors and HDL mimetics to modify HDL cholesterol and coronary heart disease risk.

Audio Journal of Cardiovascular Medicine "RADIANCE-1" Study: Cholesteryl Ester Transfer Protein Inhibitor Fails to Benefit Patients with Familial Hypercholesterolemia REFERENCE: Abstract 407-7, American College of Cardiology New Orleans JOHN KASTELEIN, Academic Medical Center, Amsterdam A drug which raises HDL and reduces circulating levels of LDL has nevertheless failed to reduce atherosclerotic progression in patients with familial hypercholesterolemia. This disappointing outcome of a study using the cholesteryl ester transfer protein inhibitor (CETP), torcetrapib, was announced at the American College of Cardiology meeting in New Orleans. John Kastelein discussed the findings, and their implications for therapies targeting HDL, with Peter Goodwin.