Decoding Medical Research

Announcer: 0:00

Welcome to MedEvidence, where we help you navigate the truth behind medical research with unbiased, evidence-proven facts Hosted by cardiologist and top medical researcher, Dr. Michael Koren.

Dr. Michael Koren: 0:11

Hello, I'm Dr. Michael Koren and I'm here to lead another session of MedEvidence in our series of two docs speaking with each other, and I'm really fortunate to have a kindred spirit here, Dr. Christopher Labos, from Montreal, Canada, and he and I are kindred spirits because we're both cardiologists, we both believe in evidence-based medicine and we both have a way of critical thinking and describing our critical thinking such that we engage patients and help people understand the truth behind the data, which is the mantra here at MedEvidence. So, christopher, thanks for being part of this program. Why don't you let people know a little bit more about yourself? And you have a fascinating background. I enjoyed learning a little bit about you and share that with the audience.

Dr. Christopher Labos: 0:59

Sure, so my name is Christopher Labos. I like long walks in the rain, cold autumn days by the fire drinking hot coffee

Dr. Michael Koren: 1:08

and pina coladas right?

Dr. Christopher Labos: 1:11

So, yeah, I'm a cardiologist by training. That was my clinical training. And then something really shocking happened to me during my clinical training, when I was going through residency, I kept asking people. I was like I was really interested in research and I was like, well, how do you do research? Like how do you do it? And they were like, oh, you know you have to recruit patients, like okay, but then how do you do it? Like how do you actually analyze the data? Once you do this chart review, like what happens?

Dr. Christopher Labos: 1:33

And I realized that for a lot of people they would collect all this data they would fill out an excel sheet, it would go into this black box and then all of these magical numbers would come out. Nobody really knew what they meant. And I started saying, like, well, how do I learn how to do research? And one of the cardiologists at Mcgill, where I did my training, uh, was also also had a phd in epidemiology. And he said, well, if you want to do that, you should do a degree in epidemiology so you can understand how it works. And so after I finished my residency, I then went back to school to do a master's in epi, which was really fun because everybody in the class was 10 years younger than me and they were you know, they were there with their laptops and I was, like you know, with my Star Wars notebook taking notes, so boy.

Dr. Christopher Labos: 2:16

I really got update because I hadn't been in class for like a decade at that point, and so I started learning about epidemiology and statistics and I was like, oh god, they do.

Dr. Christopher Labos: 2:28

they did not prepare you for this right they did not prepare you for this at all and it was a really awakening, like an eye-opening experience, because I ended up, you know, learning a lot about how research works, but I also learned a lot about how research goes badly and sort of the.

Dr. Christopher Labos: 2:44

The light bulb moment for me was we were in class and we were learning out things like about absolute risk reduction versus relative risk reduction. I was like this is really interesting. And then this article came out about and it was sort of one of the studies that came out of the think, out of Framingham I think, and it was one of these you know fairly things. They sent out a food questionnaire and it was about strawberries and berries and we found that women who ate berries were at a 22% reduced risk of cardiovascular disease, which is, you know, you see studies like that all the time and I was looking at it and then I said, oh, with my newfound statistical knowledge, let me crunch the numbers. And it turned out that you had to take like 86,000 women and like force feed them strawberries every day for two years to prevent one non-fatal heart attack.

Dr. Christopher Labos: 3:27

Right right, and so you know, when you frame it that way, probably not the most important public health intervention we're ever going to accomplish, Right?

Dr. Michael Koren: 3:36

yeah.

Dr. Christopher Labos: 3:36

But I wrote a. I was like I need to, like I need to do something about this and I wanted to get involved with science, communication, the media and one of my colleagues, his parents, were journalists, and I said what do you think I should do? He's like well, why don't you just send the letter to the editor of the local newspaper? Like well, they published that. And they were like, absolutely. And so I wrote a letter to the editor about the whole strawberry thing and absolute risk reduction, why we need to understand food research better. And their response was oh, this is really interesting. Can you cut out,3,000 words and then we'll publish it in the newspaper? And they did. And I was like so excited I had about seven copies.

Dr. Christopher Labos: 4:09

I got copies from my parents and a scrapbook and an email was like are you interested in like, can I write others? And they were like sure, we're not going to pay you, but go nuts. And so I started writing letters to the editor and then that became a regular column, and then the column became a radio segments and the radio segments became TV segments. And so I started doing that regularly.

Dr. Christopher Labos: 4:34

And then, you COVID, hit and all of a sudden you needed medical experts and because I had a track record and they knew they could rely on me, because I had a good camera and microphone set up, like you're seeing all now, I started doing the COVID updates because a lot of it at the beginning was not really infectious disease knowledge. It was about interpreting data and knowing how to separate out the wheat from the chaff Right. And you know it was just about telling people like I wouldn't really rely on this. This is kind of sketchy information, this is where the good information is coming from. And so a lot of it, you know, sort of exploded during COVID. And then I started doing a whole bunch of other things for Medscape and it just sort of started branching out into all kinds of science communication venues.

Dr. Michael Koren: 5:15

Nice, beautiful. Well, you do a terrific job, so thank you for helping to educate the public. So let's jump into some of the things that you mentioned. So you mentioned statistics, so we'll jump in statistics, because I think that's really important.

Dr. Christopher Labos: 5:30

I think we're going to lose all the viewership right now. We're going to lose it. We're going to talk about statistics. Click.

Dr. Michael Koren: 5:34

Right, right. Well, I was going to hold up back on that until the end of our conversation, but while we still have people's attention, maybe we should get into that a little bit. So I like to start with the Mark Twain line you might have heard. This is that there are three types of mendacities lies, damn lies and statistics. And you brought up a good example of it, where you read something oh, 22% reduction in mortality, and it turns out that that's three patients over 80,000 people, so obviously not meaningful. And you're getting into the concept of relative risk reduction, which is the 22% versus absolute risk reduction is the number of people who are affected. So that is a great point, a fabulous point that people don't always particularly understand well, but that's a good question to ask. And the easiest way to ask absolute risk reduction is simply how many people out of 100 will benefit from this?

Dr. Christopher Labos: 6:25

Yeah, and that's an important point. And listen, there is again not to become too nihilistic. There is a reason why relative risk reduction is used in most scientific research. That's how the math works out, right, right, and I think this is also important because it's very easy. That's how the math works out, right, right, and I think this is also important because it's very easy. It would be very easy for me to come on tools like this and be like all medical research is wrong, and there have been some people during COVID, very prominent physicians, who are like everybody is wrong except for me. I would do a much better job than Anthony Fauci or like, yeah, calm, down there, yeah exactly.

Dr. Christopher Labos: 7:25

So there is a reason why relative risk reductions are used . reduction is used there is a reason why odds  ratios

Dr. Christopher Labos: 7:26

are sort of the standard and risk ratios are used less even though odds ratios are not  as

Dr. Christopher Labos: 7:26

useful clinically because they don't have the same type of interpretation right there's reasons  why

Dr. Christopher Labos: 7:26

stuff became the default there's reasons why requent statistics became the default over Beysian stuff

Dr. Michael Koren: 7:26

yeah, exactly yeah. So let's just define some of those things for the audience a little bit. So just an odds ratio is sometimes used because it's the odds of one thing happening versus the odds of another thing happening, but accentuates benefits or harm, and so from a practical standpoint, it could look like something is much better than in fact it is, whereas relative risk is going to be less accentuating and absolute risk reduction is going to be the most conservative way of looking at what the true benefit is.

Dr. Michael Koren: 7:51

So just giving people that concept.

Dr. Christopher Labos: 7:54

Yeah, and the fascinating thing is that all of these things are correct, right. It's not as if one is wrong, it's just that they don't mean what you think they mean. I mean, for people who don't know what people think, the words odds and risk are synonyms. They're not right. If you had a dice and you rolled the dice and I ask you what is the risk that you're going to roll a four on that dice, you would say it's one in six, because you have six sides and one of them is the one. You're that, I think. What are the odds of rolling a four, though? Most people don't know the answer to that question? The odds of rolling a four on a dice are one in five, because it's one positive event divided by five negative effects. Right, the five sides you don't care about, right? Both of those are correct. It's just that our brains are not hardwired to think in terms of odds.

Dr. Christopher Labos: 8:38

Statisticians thinks in terms of odds, bookies thinks in term. Think in terms of odds, right, but the general public doesn't. So if you publish a paper with an odds ratio, you might get an odds ratio of three, and if you were to do the risk ratio, the risk ratio would be two, the odds ratio is always going to be more extreme than the risk ratio, and there's a hilarious example of that in cardiology. Actually, where they went to the uh I think it was an american heart association meeting was one of the major conferences where they asked a cardiologist like, would you send this patient for an angiogram? And uh, they answered yes, yes, yes, yes, yes.

Dr. Christopher Labos: 9:14

But they found that there was a discrepancy and they were having actors read scripts and so there was a discrepancy between the the white actors and the black said well, you know, there's a racial bias in cardiology that we need to address, which is a fair point and it's actually a true statement. But then somebody says like, yeah, but you reported the odds ratio. The odds ratio was 0.6, like a 40% difference between the white and the black actors. And well, if you look at the risk ratio, it's actually much less pronounced. It's still an issue, but you sort of, and so it's really important to really understand the significance of the statistics that you are using, because you know there's lies, there's damn and there is statistics. Statistics can be misused in the same way that anything can, and so it's important to have an actual good mathematical grounding so that you know what you're talking about, because you could unintentionally make a problem seem worse, that it's exaggerating the effect a little bit more so than relative risk.

Dr. Michael Koren: 10:29

And the most conservative concept again is the absolute risk reduction, and so that's a great take-home message for most physicians, quite frankly, and for patients, and so those are the kind of questions that you should be asking, or at least thinking about when you look at a study result. So you also use the term frequentist versus Bayesian and we had a little conversation about that before and I think, again, it's a foreign concept for most physicians, quite frankly, and maybe it's worth a couple of statements on that. Do you want to dig into that? We'll dig into that a little bit together.

Dr. Christopher Labos: 11:01

Sure, so I mean it's going to take about an hour, so like settle there.

Dr. Michael Koren: 11:05

Yeah, we want the three-minute version.

Dr. Christopher Labos: 11:07

Okay, okay, I assume this was like the Joe Rogan podcast and we could go for like four hours.

Dr. Michael Koren: 11:10

Yeah, we don't smoke pot during these broadcasts.

Dr. Christopher Labos: 11:13

Okay, fair enough, sorry.

Dr. Michael Koren: 11:16

It's not legal yet in Florida.

Dr. Christopher Labos: 11:19

Oh, okay, interesting, there you go there you go.

Dr. Michael Koren: 11:21

Another reason to come down to Florida.

Dr. Christopher Labos: 11:23

Yeah, okay, I'll give you a three-minute version of what Bayesian statistics are For anybody who's interested. There's a great book that I got I think I got it as a gift. It's called the Theory that Would Not Die. So if you're interested in a not mathematical explanation of what Bayesian statistics is, with a historical background and some very simple examples, I think's a great book. It's very accessible. It's called the theory that would not die. I can't remember the author. I read it quite a few years ago. I thought it was really good.

Dr. Christopher Labos: 11:48

Bayesian statistics named after Lord Bays, who was an english reverend of all things. Yeah, it's, it's the way of looking at data. The way most statistics work is you, you have a theory and then you try to disprove it, right? So you're basically saying, when you take the p-value, which is the statistic that we use in most medical research analysis, the p-value first of all. Nobody can actually give you the correct definition of a p-value because it's so convoluted and it's basically this I'll see if I can get it in one shot the p value is the probability that you would observe the data that you did, or, more extreme data, if the null hypothesis was true, so if there was no difference between the two medications that you were studying. If there was no difference, the probability that you would see the data that you got in your experiment is the p-value. So if the p-value is very, very small, you're like it's very unlikely that I would have gotten this data if the theory was untrue.

Dr. Michael Koren: 12:55

Right, yes, you said that beautifully. That was perfect. That was perfect. And then the convention, of course, is that we talk about a p-value of less than 0.5 as meaning significance, meaning that the likelihood that that occurred by chance is 1 in 20. But we know there's a lot of caveats to that and there's a lot of discussion around that concept. But that was beautiful. I love that.

Dr. Christopher Labos: 13:16

Yeah, and here's the thing that p-value is sort of like very arbitrary. I mean, it goes back to Ronald Fisher who just kind of, like you know, picked the number and he's like I don't know 5%, and it just became standard for no reason, right. And there's no reason why we can't lower that threshold. But the whole premise of that field of statistics, which is the standard across most of medical research, is that it's frequent statistics. It's saying if the theory is true or if the theory is untrue, depending on how you want to look at it. But that's not what we care about. We want to know whether the theory is true or not.

Dr. Christopher Labos: 13:52

So Bayesian statistics goes about it the other way Rather than starting with a premise about the theory and then looking at the data, you look at the data and you develop your theory and it says, given the data that you have in front of you, what is the probability that your theory is true? It's a different way of analyzing the data and it's actually a much more logical way to interpret the data, because it's sort of the difference between sensitivity, specificity and positive, negative, predictive value, right? So for people who are familiar with these terms, you're going to get a basic primary epidemiology. Now, the sensitivity of a says of all the people who are sick, how many of them will test positive. That's the sensitivity and specificities of all the people who are well. How many of them will test negative?

Dr. Michael Koren: 14:33

Right. So, christopher, from a clinical medicine standpoint, we usually use a Bayesian approach. So, for example, somebody comes to the emergency room because they're short of breath. they're coughing, they have a fever, we do a chest X-ray and we think we see pneumonia on the chest X-ray and we diagnose that patient with pneumonia. But we know that that chest X-ray is an imperfect tool.

Dr. Michael Koren: 14:56

But in this setting, what it does, it moves us from the presumption of pneumonia, to quote the official diagnosis of pneumonia, although there could still be some level of doubt. The flip side being is, if that patient came in and had fractured their foot and you got a chest x-ray and they have absolutely no symptoms and the chest x-ray comes back and says pneumonia, you'd be very skeptical about it. The exact same chest x-ray, but you're just moving the needle in terms of what the truth is, not actually deciding that that is the truth. But that's why clinical medicine works, whereas clinical trials works in the frequentist way that you were explaining, whereas you say okay, we think that this is the null hypothesis, this is our hypothesis and we're going to get a number of patients that come in that help us determine is that true or not true, based on this standard of evidence.

Dr. Christopher Labos: 15:46

Yeah, and I think what's fascinating is that there is a shift to make statistics more Bayesian and in fact there's nothing that stops you from using Bayesian statistics in a clinical trial. I've seen a few of them. They're not very common and that's because a lot of people are unfamiliar with Bayesian statistics and it's also because up until like the 1980s, the computers we had were just not powerful enough to do Bayesian analysis. The great, you know, wonder of frequent statistics is that you can literally do it with pen and pencil right, and that's what a lot of people do.

Dr. Christopher Labos: 16:17

So I think Bayesian statistics because, again, just to give you another example that you know, you said the x-ray one of another perfect example is stress testing right, just because the stress test is abnormal does not mean the patient has heart disease. It really depends on the pre-test probability. If you have a positive stress test on a young woman with no risk factors, well that's almost certainly a false positive, whereas if you have a positive stress test in a you know, middle-aged man who's a smoker and has high blood pressure, well then it's very likely that they have heart disease. And that's the problem with frequentist statistics it does not incorporate the pre-test probability into the analysis, whereas Bayesian statistics does. And that's why I think it's so fascinating for me and it's much more intuitive, because Bayesian statistics is how we practice medicine and yet frequentist statistics is how we analyze data and that I think a lot of people don't fully appreciate why that is so conflicting.

Dr. Michael Koren: 17:13

Yeah, it's confusing. It's really confusing for people from a number of standpoints. The flip side is that people will bring in a report that says you know, rule out lung cancer in, you know a 20-year-old who's a non-moker because they saw something on an x-ray that got them upset and you're not acting upset. So, while you're not acting upset that's what this study shows and we have that insight. That's what the physicians have is that insight to not be particularly concerned about it, based on this Bayesian concept. So really fascinating stuff. So let's take a break now and I also want to talk to you a little bit about your book, which I thought was fascinating, and a few examples from the book about how easy it is to misinterpret data and how important it is to understand these concepts so that you can make good medical decisions, which is really what our MedEvidence platform is all about.

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Transcript generated by AI

Announcer: 0:00

Welcome to MedEvidence, where we help you navigate the truth behind medical research with unbiased, evidence-proven facts. Hosted by cardiologist and top medical researcher Dr. Michael Koren.

Dr. Michael Koren: 0:24

Hello, I'm Dr. Michael Koren and I'm here with Dr. Christopher Labos in our second session of discussions about clinical evidence uh and really most importantly is what we discussed uh previously was this concept of concept of Bayesian versus frequentist ways of looking at the world. And the reason this is a great conversation, Christopher, is because you wrote this fabulous book where you look at some of these concepts and how easy it is to misconstrue things. Thank you for that plug. We love that. How easy it is to misconstrue things because of your frame of reference and the way you're thinking about looking at data and you made some fabulous points about that and I want to drive those points home with specific examples in our second opportunity to discuss these things. So first let me start with a very simple question, and this can lead into this important conversation Is salt good or bad? For you, Just answer the question. I don't want to hear a bunch of stuff Is salt good or bad? Can you answer that question? Is salt good or bad?

Dr. Christopher Labos: 1:20

Yes, I can, in the same way that you answer most questions in medicine. It depends,.

Dr. Christopher Labos: 1:51

After being there, it's like we do consume too much sodium in Western civilization as a whole. Okay, and the thing about sodium is that if you eat too much of it, you retain water, it increases your blood pressure, it puts you at higher risk for heart disease and if you have heart failure or kidney failure, that is very problematic. And one of the best ways for somebody who has heart failure, kidney failure, to maintain fluid balance, not become volume overloaded, is to minimize the amount of sodium you take, because the more you take in, the more you have to get rid of to maintain fluid balance. So overall, we should be consuming less sodium. And part of the issue because one of the book chapters is about salt, one of the issues is really to sort of push back against this attempt in recent years to rehabilitate salt, to pretend that, oh, some salt is good for you, and I get into some of the findings from the PURE study and all of that, which were you know it needed some caveats. That being said, you obviously need some degree of sodium to be alive. It's just that most of us are going to get enough sodium from the food that we eat without adding table salt to it, and an important point here is that when you look at North American populations, most of the sodium that we consume is not the sodium that's present in food, and it's not even the sodium that we add at the dinner table, although that is an important contribution. Most of the sodium that we eat is added to our food by somebody else, because we eat a lot of processed food or we eat out at restaurants, right, and I think the issue with sodium and with salt is a public health one, not dissimilar from the issue with trans fats, which is the government or FDA uh uh has to really get involved to reduce the amount of sodium that is added to our food, and they've sort of taken they've already started to take steps in that, because they are trying to replace sodium with potassium and sort of pass legislation that will allow food companies to replace sodium with potassium and still call it salt. So we'll require label changes and I think there is quite a bit of evidence that substituting sodium for potassium is better, right, it reduces blood pressure. It improves cardiovascular outcomes. We've seen, you know, studies in China on that. So I think that's where we have to get forward.

Dr. Christopher Labos: 4:14

And so we have to make a distinction between the macro public health implications of having less salt in our diet from processed foods, but also realizing that some people need salt. I mean I have, you know, young patients, uh, you know, very low blood pressure. They have multiple syncopes. You know, they do need some salt just to get their blood pressure up. Right, elite athletes who need to rehydrate, like if you were to run a marathon. I've run. I haven't run a lot of marathons; I've run two in my entire life; congratulations, yeah, yeah.

Dr. Christopher Labos: 4:46

But what they tell you is you can't just drink water when you're running a marathon. You will eventually become hyponatremic, right. And you know, when people die during marathons, there is, you know, at least one of the contributing factors. It probably is hyponatremia. You do need to replace some of those electrolytes Why was it. Gatorade, You're in Florida. invented? It was invented to be an electric replacement for, uh, the gators, right? So you need electrolyte replacement if you're an elite athlete. But what's true for an elite athlete is not true for the vast majority of the general population and sort of that nuance is, I think, important to realize that you can have multiple parallel conversations. You can acknowledge that athletes need to rehydrate and replenish electrolytes while at the same time applauding the FDA for taking steps to limit the amount of sodium in the food we eat, because it is starting to become a bit excessive.

Dr. Michael Koren: 5:45

Right and it gets complicated, in fairness, for the average patient with congestive heart failure. For example, you mentioned replacing sodium with potassium, which in general is a good thing, unless your kidneys don't work that well, yes. And then it gets even trickier when you have a heart failure patient that you want to have a certain baseline level of pharmacological therapy that lowers blood pressure and you need something to lift blood pressure a little bit. So there's even be a subgroup of heart failure patients that we have to tell well, you need to take a little bit extra salt. So this is the tricky part of medicine in general, of medicine in general. But conceptually, just to make it very clear for the audience, is that for the average person that is dealing with high blood pressure or has a tendency to develop swelling or a fluid overload, salt is not a good thing. But in certain circumstances, for example athletes or people that have POTS or autonomic dysfunction, salt could be a lifesaver.

Dr. Michael Koren: 6:50

So again, it's circumstantial and should be geared towards individual patients and don't fall into the trap that say everything is good or everything is bad. And we see this for the COVID vaccines. People say, oh, the COVID vaccines are terrible, you're going to get pericarditis, or COVID vaccines are lifesaving. You're an idiot if you don't get it. Well, it depends on who you are and your circumstances. So thank you for your book that helps people understand some of these issues. It's super important for people to read things that people like you put out to help explain these very interesting, complex but really tangible issue. If you take the time to understand why we say what we say.

Dr. Michael Koren: 7:35

So, let's switch gears just a little bit. Let's talk about cardiac inflammation. Yeah, and as a cardiologist, give me what you're telling your patients these days about it. And some of the data to generate information about Colchicine, which is a drug that we use to treat vascular inflammation, came out of Canada. So I don't know if you're a part of that or not, but just talk to us a little bit about your experience, your thoughts in the inflammation hypothesis.

Dr. Christopher Labos: 8:04

Yeah. So I sort of have two sort of minds on the inflammation hypothesis. The reason why the word inflammation makes me nervous is that it is so often used by people you know who are trying to sell junk cures, right, like a series of alternative medicine. People who are like inflammation is the cause of everything. Yes, but also no right.

Dr. Christopher Labos: 8:28

So if you have arthritis, yes, it's a problem of inflammation in the joints, right, there's ways to treat it. It doesn't mean go buy my herbal supplement for $80 off the internet and I think that's where I like. So I always try to tell people is like, yes, inflammation is a problem, but one of the best things you could do to reduce inflammation is quit smoking. Please, for the love of God, quit smoking, sir you know. So it's like inflammation is a problem.

Dr. Christopher Labos: 8:52

The issue is that we don't actually have very good targeted therapies to suppress inflammation. That being said, if you can get somebody to quit smoking, start exercising, lose weight, eat healthier, their inflammatory markers will come down right. So you can affect inflammation just by being healthier. And so the question becomes is it a marker of disease or is it an independent causative agent? I think it probably is to some degree causative, and if you had a good medication that could lower inflammation, then that would actually make a difference. Right now we are sort of limited in that we can't attack inflammation directly. We just control blood pressure, control cholesterol, treat diabetes, get people to quit smoking, get them to exercise, and that does quite a bit, but there is still some residual risk there and so and it's not as if people haven't tried right there were two major trials like Paul Ritker.

Dr. Michael Koren: 9:45

Paul Ritker yeah, Canakinumab, the , the Cantos study yeah.

Dr. Christopher Labos: 9:50

Yeah, and you know there were two right. One didn't work and then there was a new investigational agent that did actually lower the risk of cardiovascular disease but was never brought to market because there were more infections and more deaths associated. So that's always the issue, right If you have a medication that's very good at suppressing inflammation, it's also going to suppress your immune system and put you at higher risk of infection. So you know, the Colchicine thing is interesting because the data is certainly positive in that trend. It's not uniformly positive and the trials were very different. Some were in acute MI, some were in chronic CAD patients. And colchicine is, you know, has its side effects right at high doses it can cause diarrhea, , it can cause neuropathy.

Dr. Michael Koren: 10:33

What percentage of your patients can tolerate 0.6 milligrams BID of colchicine?

Dr. Christopher Labos: 10:40

Not many, even the patients who get it for pericarditis. When they have pericarditis they don't 50%.

Dr. Christopher Labos: 10:46

Yeah, I don't know, it's hard, it's easy. Not all of them can tolerate it, and so that's the thing. I think, if you're going to make the argument that there's a role for targeting inflammation in heart disease, I think that's fair. Is colchicine the drug that's going to get us there? I think if we have this conversation 10 years from now and there's a new drug, we're going to look back and be like it's sort of like imagine if we were having this conversation in the late 70s or early 80s and we were talking about cholesterol and we were talking about the cholesterol hypothesis and we were talking about giving patients like cholesteramine right, which was the drug used in the coronary primary, coronary primary prevention trial, right?

Dr. Christopher Labos: 11:25

All these debates about cholesterol in the 70s and 80s and into 90s people arguing is cholesterol true or not, was largely driven by the fact that we just didn't have very good medications to lower cholesterol, and then statins, and PCSK9's the come in and it's like well, what you know? There's no argument anymore, right? Exactly, driving cholesterol down to near zero levels, right? Huge benefits, right, the? You know the debate is largely over.

Dr. Michael Koren: 11:48

people still debate it.

Dr. Christopher Labos: 11:49

Yeah, there's a few fringe people out there. Let's be fair, some of them are cardiologists, which is worrisome, but that's the thing it's like when people are arguing about inflammation. I think part of the reason why we can't come to a consensus is that we don't have a very good therapy. If you had a good drug that was a really powerful inhibitor of inflammation that you could test in a trial, then you would get an answer and we would be good.

Dr. Christopher Labos: 12:14

I think the problem is now is that you look at the Colchicine data and it's like you know, is it the first medication I start? No, because the reality is we have so many different targets and especially if you're managing patients, you know you got to control their blood pressure, got to get their cholesterol down, you're going to do their diabetes, you got to get them to quit smoking. You have so many, I think, so many more important targets first, that it's sort of at the lower end of the spectrum. And a lot of patients, what do they say to me? I'm taking too many medications, right, right, so what's the highest priority? Obviously the antiplatelet. Obviously the cholesterol, obviously the blood pressure stuff. Right, sglt2 inhibitors if you have diabetes. So you know.

Dr. Michael Koren: 12:54

Or not. Obviously, sglt2 inhibitors are very helpful in heart failure. Just to make sure people understand, with or without diabetes. But sorry to interrupt.

Dr. Christopher Labos: 13:03

Yes, yes, yes, absolutely. And so that's the thing. It's. Like you know, sometimes the practical has to give way to the ideal. In an ideal world, would you want to add on colchicine to somebody who has stable CAD? Yeah, in the practical world, where cost is an issue, there are practical considerations, there's polypharmacy, there's side effects. Sure, I don't know that we're there yet. But then again, 10 years from now, maybe we're going to have a specific, you know, interleukin inhibitor and we're going to be like, wow, that was a silly thing we said 10 years ago. Obviously, inflammation is a big deal, and I really do see a lot of parallels between inflammation and the cholesterol debates of the 70s, 80s and 90s.

Dr. Michael Koren: 13:41

Well, as you know, we're working on that.

Dr. Michael Koren: 13:42

As a clinical trialist.

Dr. Michael Koren: 13:43

I'm involved in a lot of trials as we speak, looking at different markers of inflammation and how to suppress those markers and whether or not they result in cardiovascular benefits.

Dr. Michael Koren: 13:52

So to your point, in five or ten years we'll have a really good read on these things and understand if blocking a certain interleukin specifically is the answer to this question. And it's fascinating and from our standpoint here in Florida and other sites around the country that we work with, there's opportunities for patients to get involved in these clinical trials and they may or may not get some individual benefit, but they'll be part of this process of really helping to understand really the next frontier in reducing cardiovascular morbidity and mortality. So, as we know, we've done a really good job over the last 30, 40 years of reducing heart disease death rates by 50% or more, but there's still a fair amount. It's still the number one killer of people in Canada and in the United States and most places around the world these days. So there's a lot more that can be done and maybe the inflammatory hypothesis is the way to achieve better results in the future.

Dr. Christopher Labos: 14:48

Yeah, yeah, agreed. It's going to be really interesting to see what pans out over the next five to 10 years.

Dr. Michael Koren: 14:52

Yeah, Christopher, this was an amazing discussion. Thank you, I loved every minute of it. You're a wealth of knowledge. Show your book for everybody again, because I would recommend this book Again. It's a thinking person's book and it's something that it's worth reading and helps you understand the statistical knowledge that people like Christopher have and how you apply that in your day-to-day life. And, more broadly, the MedEvidence platform is to help people understand why these controversies pop up about is salt good or bad, is coffee good or bad? And usually the answer is just the answer that Christopher just gave, which depends.

Dr. Michael Koren: 15:31

It depends on who you are, what the circumstances are and again, from my perspective as a clinical trialist. The most fascinating thing that I do is, when we don't know the answer, we have a way of testing it.

Dr. Christopher Labos: 15:43

Yeah, and I'll just throw in one thing about the book, lest the Math Throw People Off. It's actually very similar to what this podcast is, because it's a conversation, right? The book is written as a narrative piece of fiction where the main character who is a doctor not based on me.

Dr. Michael Koren: 15:58

I keep claiming that, even though no one believes me, even though he looks just like you, he looks just like me.

Dr. Christopher Labos: 16:03

The main character is a doctor. He's actually attending a medical conference, and over the course of the weekend he bumps into a lot of people and he keeps having these conversations, and so each chapter is about a food, it's about a specific epidemiological concept and it's about all these controversies, but it's written like a conversation. So if you wanted a more lighthearted, easy read, that's what this book is about, and then, as a fun little twist, there's also a romantic subplot in there which I said I didn't get to that yeah, yeah, it's, it builds.

Dr. Christopher Labos: 16:34

It builds sort of midway through the book. And it happened because when I showed an early draft of it to a friend of mine, it was a doctor talking to a barista about coffee and my friend said, uh, oh, great, very interesting concept. But I kept expecting him to ask her out at the end. And the minute she she said that I was like I'm actually writing a romantic comedy and I didn't realize it, and so the whole thing sort of dovetails into a narrative.

Dr. Michael Koren: 16:57

Well, even better. That's fabulous. I love it Well. Thank you for sharing that information. Definitely, we support your efforts to hopefully sell lots of copies of this book and, most importantly, share the knowledge that's behind the book. Thank you very much for this really insightful discussion, and we'll do it again, and one of the things that I think a lot of people might be fascinated by is some of the differences between the US healthcare system and the Canadian healthcare system, and we can have a lot of fun having those discussions and how we can learn from each other.

Dr. Christopher Labos: 17:27

Yeah, absolutely, I'll be happy to come back anytime, thank you.

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