A Spotlight On: Genomic drivers of heart attacks – Akl Fahed
Akl Fahed, MD, MPH is a physician, scientist, and innovator. His research at Mass General Hospital and the Broad Institute of MIT and Harvard focuses on using genomics and data science to improve understanding, prevention and treatment of coronary artery disease. Akl talks about polygenic risk scores and the democratisation of genomic data.
Please note the transcript has been edited for brevity and clarity.
FLG: Hello, everyone. And welcome to the latest a spotlight on interview. Today, I’m joined by Akl Fahed and we’re going to be talking about the genomic drivers of heart attacks. So Akl if you could please introduce yourself and tell everyone a little about what you do.
Akl Fahed: Thanks Poppy for having me. Great chatting with you today. I’m a cardiologist and a scientist in genetics. So, I spend some part of my time taking care of patients who have had a heart attack. I’m based in Boston, at Mass General Hospital and Harvard Medical School. And then I spend the bulk of my time really trying to understand why those patients actually had a heart attack and how we could prevent it in the next patient that comes over. To do that, I try to leverage all the data at my disposal starting from what behaviours they’ve had in their life. What did we know about them before they had a heart attack? And then their DNA information, which is information that we have, that starts from the time they are born, and try to put the whole picture together to see could we really prevent the heart attack and the next patient that comes that comes over?
FLG: That’s fascinating and so important. So for a bit of background and context, how many people do heart attacks effect? How important is it for us to stop this? And more broadly, why are people having heart attacks?
Akl Fahed: Extremely important question. And the answer is that still unfortunately, heart attacks remain the number one cause of death in the world. Despite massive efforts and understanding of heart attacks, preventing it, with things like preventing and reducing smoking all over the world, etc, it still remains the number one cause of death. And just to give you a little bit of sense of, you know, when we say it is the number one cause of death, what that means if you look at it globally, all people who die every year 1/3 of the deaths every year, all over the world, is due to a group of conditions called cardiovascular disease. Now, that includes heart attack plus other cardiac conditions. But in that group, the most common driver is actually heart attack, whether it’s a heart attack that leads someone to die suddenly, or a heart attack that leads to failure of the heart muscle and eventually leads to death. And then there’s related conditions, such as having a stroke, which essentially is a heart attack of the brain. But again, 1/3 of the death in the world is due to cardiovascular disease.
Now, in certain countries where you have more data, you can start breaking it down and understand, which ones are truly heart attack that leads to immediate death, versus another heart attack that happens in someone who’s already had a heart attack, etc. And the United States is one of those countries that has that level of statistics. And the most recent statistics basically says that one person has a heart attack every 40 seconds. If you think about it, from the time we started our conversation here, there’s two or three people who’ve had a heart attack. So this unfortunately continues to be a major problem all over the world.
FLG: And there are very complicated factors why people have them. So can you define some of the more genomic drivers of heart attacks?
Akl Fahed: Yeah, I think the fascinating thing about heart attacks is, we have a pretty good understanding of a lot of the mechanisms that actually lead to heart attacks. And as I mentioned, at the beginning, we’ve done a lot to prevent heart attacks, reduce the prevalence of it. But at the same time, there’s still a lot to learn. And I would say, maybe starting with the non-genomic drivers, so those are the common things that most people actually know about, things such as your cholesterol, your blood glucose, your blood pressure, smoking, bad diet, not exercising. And I would say there’s still a lot of, there’s still a lot of room actually, to improve them.
Unfortunately, what we’ve learned is knowing about them doesn’t necessarily mean that actually everyone is following them. Even in some of the best places in the world, you’ll see that a lot of people that should have lower cholesterol, don’t. And so there’s a lot of room there for massive public health efforts to really try to make changes to people’s lives and behaviour so that we can reduce the burden of cardiovascular disease, because we know those approaches actually work.
Now, sometimes you look at people and they’ve had all those risk factors actually controlled, yet they have a heart attack. And you start wondering why and those are the observations that really led a lot of people to say, well, there must be something here, if all those risk factors are controlled and they’re having a heart attack, and other people who actually are kind of more protected. Maybe they do smoke etc. We all know the anecdotal, “My grandfather smoked until he was 90 and never had a heart attack and it was good genes.” So indeed, there’s a genetic factor. And what we’ve learned over the years is that there are multiple genomic drivers of risk and heart attack.
And when we talk about risk, we talk about risk and we talk about protection. So it’s a spectrum. And, we like to classify those drivers into three big groups. One is called monogenic drivers and monogenic drivers is the most well understood mechanism of genetic risk for heart attack. And that means that at a single point of someone’s DNA, there’s a defect. And that defect leads to a gene that just does not behave as it’s supposed to behave. And in the case of heart attack, that most common gene is the LDL receptor. There are other genes, but this is the one that’s most commonly affected. And then the end result of that is that the LDL cholesterol does not go into the liver, but it stays in the circulation, and then deposits in the arteries and causes them to get blocked and then causes a heart attack. Now, this genetic cause of heart attack is extremely rare. It’s not common, if you look at the level of the population, it’s less than half a percent.
Then the question is, many more people are having heart attacks, or why are they having heart attacks. And what we’ve learned is that there’s another mechanism that we call the polygenic mechanism of risk. And that’s a mechanism that’s well known in a lot of complex diseases. Those are things like heart attack risk, diabetes, obesity, etc. And that mechanism is very different. Instead of having one single point in your DNA that has a very large effect, you actually have many, many points across your DNA, there could be millions of them, and each one of them might increase or decrease your risk by just a little bit. So it could be increasing it by half a percent, and another one is reducing it by 1%. And then what we’ve learned is, for single individuals, if you sum up all those variations across your genome together, you end up with a score that we call the polygenic score. And that score, if you look at it at the level of the population, you start identifying individuals with high scores at increased risk and individuals with low score at reduced risk. And then most of the people are going to be in the middle they have average risk.
But really, one of the landmark studies that came out of this from Boston was really a study that actually showed that for a large segment of the population, depending where you draw the cut off somewhere between 8 and 20% of the population do have at least double the risk compared to everyone else. Sometimes triple the risk if you look at the 8% cut off. So that’s a lot of people, that’s the same risk that actually a single point in the DNA from the LDL receptor actually increases the risk. So we’re seeing a lot of people have a high risk due to the polygenic mechanism.
The third mechanism is more recently understood. And it’s different from DNA that you’re born with from early in life, it is an epigenetic mechanism. These are changes that are happening to your DNA as you grow older and it relates to the interaction of the environment to your DNA. So we call them somatic mutations, those are also single point changes in your DNA, that whose end result is actually leading to mechanism called CHIP, or clonal hematopoiesis of indeterminate potential. And what that means is a group of cells, those are hematopoietic stem cells, or blood stem cells, that actually get expanded in an abnormal way. It’s essentially the same mechanism that leads to some cancers, but in this case, it is expansion of those cells without blood cancer. And people who have CHIP due to those variations, they have double the risk of heart attack. And it’s now become understood that this is related to increased inflammation in their bodies. So now we can measure those variations, we can quantify that risk. And there’s a lot of efforts done in that regard, it’s not common, those mutations are present in 0.5 to 1% of the population. But again, collectively, we’re thinking now of three genomic mechanisms where people can have a risk of heart attack the monogenic, the polygenic and somatic changes in the DNA.
FLG: And risk is such a complicated concept to communicate. And obviously, doctors are often having to have that conversation with patients anyway. I think with single genes, especially terms like the BRCA genes, they’ve become fairly well known in the public sphere. But these polygenic scores have a lot more nuance if we’re talking about public benefit and patient benefit. How can we communicate this risk effectively?
Akl Fahed: Yeah, this is a very important question. And it’s one of the biggest challenges of using polygenic scores in clinical practice. For decades, we’ve known how to communicate risk from a monogenic cause from a single defect, it’s been black or white, you have it or you don’t have it, it’s very easy to understand. If you have it, you have increased risk, if you don’t have it, you don’t have increased risk. And we’ve understood that, we have an entire infrastructure of genetic counselling, clinical geneticists, that actually know how to report those, both from a laboratory perspective, but also from a patient communication perspective.
With polygenic scores, that entire infrastructure needs to be built and understood. Going from the epidemiology and statistics that the kind of research that my group does, to really moving forward towards making it an actual test that the lab can run, but then also understanding how do you communicate it to patients, which I think is what you’re alluding to. And the communication itself is not straightforward. Because unlike the monogenic causes, you need to communicate this to pretty much everyone, because everyone has a polygenic score. And then when you communicate it, you need to be able to have that individual understand the risk and act on it appropriately. You know, there’s a lot of complexity and how you do that?
So, how do you communicate someone at threefold increased risk versus a twofold increased risk? And what does twofold mean, when you talk about relative versus absolute risk? It brings a level of complexity. How do you integrate the polygenic score with all the other risk factors that you know about? What if someone has a single mutation and a high polygenic score? What if someone is a smoker and has a high polygenic score or non-smoker with a low polygenic score? So that adds multiple layers of complexity.
I would say there’s been massive developments in that space trying to get to clinical implementation and trying to answer some of those questions. Some of that work from our group here in Boston, but also from many others. So, for example, we’ve done some work on looking at the interplay of a single variation, the monogenic and polygenic goals, and how they come together. So we have a little bit of a framework of understanding of how to report it if you have both.
Another thing that we are working on is also how does it interplay with your clinical and lifestyle risk factors? How do you actually bring them all together in a single number to the patient? The third thing is actually, how do you build even a report that you can offer to patients? How should that look like? How would people understand that? So we’ve gone down to the level of doing focus group discussions, designing reports, working with designers, working with genetic counsellors and testing how people actually behave when you return that results to them.
I would say, overall, there’s way more work that needs to be done. I would say the early results are encouraging. What we’ve seen in at least most of the studies that have been done is that people interpret it in a positive way and it does serve as a motivating factor. We always worry about the risk of correct information. So you give people correct information, but maybe they interpret it incorrectly. You get a high genetic risk result and you say, “Well I have a high genetic risk so, it’s not worth it, I’m gonna go back to smoking”. But that doesn’t happen frequently. You know, anxiety is not as high usually when you return those results. And overall, I would say early data of returning results is encouraging.
One thing that needs to happen related to the return of results, is really a more prospective follow up to see what happens when you return that result to many people. So larger sample sizes, what happens prospectively. We’ve had those studies, we have a study right now on 100 people that that is being prepared for publication, there have been few other prospective studies. But there are massive efforts in the UK and in the US to actually determine that at scale. And understand that and things you’d be interested in, how does that actually affect outcomes down the line. And I think that’s really where the field needs to move, including clinical trials that actually enrolled people based on their high genetic genomic risk information, and then try to test certain interventions prospectively.
FLG: And that comes with a lot of empowering the patient. You’ve talked about some of the methods there with genetic counsellors in order to help empower them and support them. But that extra involvement often puts more responsibility on them as well. So what are the key challenges you think we’ll see as we shift our systems to these patient led ones? And with these sometimes complex diagnoses? And alternatively, what is your dream patient led system?
Akl Fahed: Yeah, I’m glad you’re talking to using the word patient led system, because surely, that’s what I believe in. And that’s my personal opinion, I know many people won’t agree with it. But I personally think health systems could have done better in prevention in many ways. You look at a lot of studies every day, where the entire themes of those studies are gaps in care, and then management. And you can’t but wonder if there are that many gaps, even for things we know that absolutely work and are lifesaving, such as lowering someone’s cholesterol when it’s high. Or major gaps, you look at studies where like 30 to 50% of people who qualify are not getting the medication for a variety of reasons. And that’s not related to genetics. Its pure, known clinical interventions that we know work and save lives, and we’re not doing enough of them. It makes you wonder could this be better if it’s in the hands of patients?
I’m personally a big believer of what we call the consumerism of preventive care, in the sense of really making those efforts patient led, driven by patients, empowering patients to take ownership of their prevention, educating patients, and making it easy to access that information. And really access preventive efforts for disease in general. And obviously, cardiovascular disease is one big group. So, if you ask me, what is my dream, I think genomics should follow the same path. You know, this should be in the hands of patients, to enable them and empower them to make the changes with appropriate guidance. And I think with technology, with software, with algorithms, this is not hard. This could be done to empower and educate patients to really take ownership of preventive care. Because I think the other model, we know how it performs. We’ve had enough chance to test it over decades. We know exactly what the comparator is, I think we need to start building comparison groups using patients as consumers of their own health and care and seeing how that actually performs compared to current standards where, it’s mostly doctor driven and my hypothesis is that I think we would be better off if we put it in the hands of people.
FLG: Fantastic. And some of the stuff you’ve already touched upon, it sounds like large datasets are going to be essential for us to gain that population wide foundation. So what are the obstacles in gaining that reliable data and analysing it?
Akl Fahed: Yeah, thanks for bringing that up. The issue of data and access to data and availability of data, I would say that’s been one of the best things that happened in medicine over the past decade. This increased availability of large datasets to as many researchers as possible so we can actually talk about the things that we’re talking about today. This would not have been possible was it not for large efforts, such as the UK Biobank, which I think really revolutionised how we think about data, both genomic and nongenomic, and improve their understanding of disease in remarkable ways. I think, the UK has led the way with the UK Biobank. I think other countries like the United States are following suit with a lot of efforts, such as the All of Us project. But also many biobanks and hospitals, national biobanks and multiple countries in Europe – I think those datasets are really changing the way we think about medicine and enables it at a much more rapid pace than we did before. Basically, whatever used to take 10 years to discover now we can discover it in a year, if you make the data available. There’s a simple formula – just make the data more available to more people, and there’s always going to be a collective benefit. That requires a mentality change, requires innovation, requires people to really believe in that vision. Unfortunately, not everyone does.
So, those are unique models in the UK, United States and multiple other countries in Europe. But the issue remains is that if you look at the world population, it is a very skewed representation in those data sets towards individuals of European ancestry, partly because mostly it’s countries in Europe, and the United States that are fighting that effort. And I think this is a major issue, because you end up with a large disproportionate representation in those datasets for individuals all around the world. And that’s very important in genetics, because genetic ancestry matters in a lot of those discoveries that we make. There’s always a risk, if you take those discoveries and implement them at scale, you’re gonna worsen healthcare disparities by really implementing models and scores and information discovered from individuals of European ancestry. And I think that’s a major issue.
So what we need is essentially, same as we have the UK Biobank, just have similar models all over the world and different countries with data access. So you know, in Africa, in Asia, in the Arab world, Middle East and the Levant. Again, Arabs represent 5% of the world’s population, and we have nearly no publicly available genetic datasets at scale to look at – very, very little in the order of 1000s. And that’s hard when you look at hundreds of millions of Arabs around the world. It’s the same thing with individuals of African ancestry. So that, in my mind is the biggest challenge, but I’m hopeful that those models, such as the UK Biobank, US based models, are starting to really prove to the world that this is the way to go. And then really improve and enhance data availability, and data collection from all over the world.
FLG: And on that note about diversity of data and to kind of return to heart attacks, we know that men and women respond differently, present differently. So how feasible will it be or do you think it will be to implement integrated solutions equally to a range of groups? And bearing in mind these limitations and potentially other limitations we haven’t discussed?
Akl Fahed: In my mind, there are three groups. There are sex based differences, so men and women, there’s genetic ancestry differences, that’s very specific to genetics, but there’s also environmental differences related to geographic locations. So also where the population is coming from. So you know, if you study African-Americans in the US and you study people living in Africa, it’s going to be very different. And so in my mind, you know, the ultimate solution for all of that is representation in the training dataset. So that means in the original discovery datasets, where we build all those models, and I think that should be the number one effort. That should be what everyone is striving towards – just more data availability, that is representative. Now, I would say that takes time. And you’ve got to see what else can you do to improve and to help them in that direction. The way I think about it is, it’s like a true north. And then in order to get there, you need to make small incremental efforts. And I think there’s different efforts that could be done.
Obviously, the biggest effort should be done on recruitment and improving diversity. But along the way, there are a lot of things that you can do in the way you treat the data, the way you publish the data, the way you execute on those models. If you’re developing a model, you always need to look at sex based differences in my mind, and make sure you’re not using a dataset with 80% men and 20% women and then ending up creating a generalised model that’s biased and going ahead and implementing it. When you implement, you need to also implement in diverse groups. And if you don’t, you need to recognise that as a major caveat and say, “This only applies in that setting, and I need to build and implement in another setting to be able to test that.” So in my mind, there’s incremental steps to move in that direction.
Some are going to be on implementation, some are going to be on how you analyse the data. And then some are going to be, which we’re seeing a lot with polygenic scores, on improving the methods. So, a lot of the statistical methods can try to make up for improving the performance of a lot of the polygenic scores that we use, and in certain ancestry groups they do that very successfully, to the extent that certain scores perform equally to scores in Europeans. That’s solely based on a little bit more datasets, but also proof computational methods to get there. So, again, incremental measures to get to that final goal.
FLG: And you’ve just mentioned computational methods as well. Machine learning is often touted as the solution to data overwhelm, how realistic and attainable is it in your field? And what are the limitations we might see?
Akl Fahed: I think it’s very realistic. Actually, let’s talk about the hope of machine learning, and what it is allowing us to achieve. And then talk about the limitations. From a hope perspective, same as increased availability of genetic data, there’s increased availability of all data, and improvement in our understanding of, how we digest and work with this data and apply machine learning models. And I would say that opportunity is being really leveraged to improve our understanding of disease in many ways.
So I would say some of the most obvious ways in my field and cardiovascular disease are really around understanding the phenotype, the disease itself better. So right now, when you think of coronary disease, or heart attack risk, most of the datasets are labelled as a binary phenotype as yes, no, someone had a heart attack or did not have a heart attack. But as a you know, as a cardiologist who treats heart attacks every day, I can tell you that no two patients are alike. Every single patient is different to the next one that’s going to come over. And you know, we don’t really capture that a lot in our genetic studies. And one of my main efforts is really thinking about how do I capture that variation in the phenotype. And machine learning is a very powerful technique that allows us to do that.
So, a lot of the efforts I would say that are happening in cardiovascular disease is using machine learning to understand raw imaging data from patients. So you take someone’s image of their heart, the cardiac MRI, or you take the MRI angiogram, which is the test that you do to actually diagnose heart attack, where you actually see every single artery in a three dimensional way and try to use that data as input to try to understand the phenotype better. And then mixing that with genetic data. -This is really where you start getting a better understanding of mechanisms of disease, and specific populations that might be at risk and might need to be treated differently. I think this is really where the promise of merging genomics with machine learning on imaging data to understand biology better is happening. And there’s been really multiple exciting papers in that space. I would say mostly from cardiac MRI and cardiac echo data, or cardiac ultrasound data that really shed light on new discoveries of biological pathways that we didn’t really appreciate, before using traditional data. So I think that’s kind of how I think about machine learning as a major promise.
Obviously, there are multiple other applications that are not related to risk prediction, those are things such as automated reading of imaging studies, and improving flow. So those are, machine learning applications to actually improve how we care for patients immediately. In my field, when we evaluate the arteries of someone, often we have to put wires inside their own arteries and do detailed measurements. But now there are machine learning models that are actually predicting or guessing those measures without even having to put in the wire. So that reduces the procedural time, allows us to do it on more people, reduces the risk of the procedure itself. So again, those are machine learning applications not to predict disease, but actually to help us when we take care of patients, and doing it in a more efficient way, in a safer way on more people, and getting more data. So I think that, it’s not just a lot of promise, I would say there are actual applications that are happening with machine learning. So I don’t think it’s to say machine learning is the future, it’s actually the present.
Back to the limitations, it’s not all a rosy picture. I think there are limitations we have to consider. When we think about machine learning models, and how we’re using them to predict or build models, essentially, in the future, you have to always keep in mind that the models are always going to be as good as the data that you give them. So again, it goes back to exactly the earlier conversation we’re having on data quality, and data diversity. If you have biases in the way you practice, and then you’re used to building a model based on that, the model is going to predict the bias. It’s really about trying to pick up on those biases. And then you could risk worsening those biases by building models that actually understand them and act on them. Those are things such as you know, race in the United States, and we know we do have discrepancies in care based on race. And if you’re implementing models that are learning from those discrepancies, then we are actually going to propagate them. That’s a major risk that people need to be cognisant of as we use those models. We need to be very critical of what they are doing, how they are doing it and what applications we’re using them for.
FLG: Absolutely. And something that I think is threaded throughout your interview today. So I’m intrigued to pick your brain. What are your thoughts on the democratisation of genomic data?
Akl Fahed: Yeah, I kind of alluded to that earlier. It might not be a very popular opinion, but I am all for it. I do think democratisation needs to happen in two ways. One is the democratisation of access of genomic data for scientists, because that will really increase the pace of discovery. And we’ve got to be fast. I mean, one thing that the COVID pandemic taught us is that we just don’t have time. We’re getting better and better over the years, we’re way faster at drug discovery than we were decades ago. But we’ve got to be even be faster to beat disease because the world keeps throwing bad things at us. So, I really believe in democratising access to data, making it easily available to scientists, so that you can use the collective brain power of scientists all over the world is critical for us to beat disease. So that’s one way of thinking of democratisation of data to scientists.
The other way of thinking of democratisation of data is to patients. Again, I’m a big proponent of consumerisation of large parts of healthcare. Preventive care is one of them. My hypothesis is that if we put it in the hands of patients, and we educate them, we empower them, we will be better off than we are today.
And the future in my mind for genomic data is going to be a genome first approach or preventive genomics approach where everyone has their DNA information. And you have tools that can educate you and guide you to how to use that as you go on with your life. And because it’s available data that freely stratifies people at risk, why not use it early on? I think we still have multiple milestones to get there, but that that would be my personal vision.
FLG: Thank you so much. I’ve learned so much on everything from polygenic scores to machine learning and the democratisation of genomic data. Thank you for taking the time to shine a spotlight with us today.
Akl Fahed: Thank you so much Poppy. I really enjoyed our conversation and I appreciate the time to chat with you and your audience. Thank you.
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